
@book{bartonitz_agile_2018,
	address = {Berlin, Heidelberg},
	title = {Agile {Verwaltung}: {Wie} der Öffentliche {Dienst} aus der {Gegenwart} die {Zukunft} entwickeln kann},
	isbn = {978-3-662-57698-4 978-3-662-57699-1},
	shorttitle = {Agile {Verwaltung}},
	url = {http://link.springer.com/10.1007/978-3-662-57699-1},
	language = {de},
	urldate = {2022-08-20},
	publisher = {Springer Berlin Heidelberg},
	editor = {Bartonitz, Martin and Lévesque, Veronika and Michl, Thomas and Steinbrecher, Wolf and Vonhof, Cornelia and Wagner, Ludger},
	year = {2018},
	doi = {10.1007/978-3-662-57699-1},
	keywords = {Bildung, Multimedia, FernUni-Hagen:MABM:Master-Arbeit, Promotion:Literaturanalyse, Charité:Promotion},
	file = {Bartonitz et al. - 2018 - Agile Verwaltung Wie der Öffentliche Dienst aus d.pdf:/Users/jochenhanisch-johannsen/Zotero/storage/CZL6W8LV/Bartonitz et al. - 2018 - Agile Verwaltung Wie der Öffentliche Dienst aus d.pdf:application/pdf;Bartonitz et al. - 2018 - Agile Verwaltung Wie der Öffentliche Dienst aus d.pdf:/Users/jochenhanisch-johannsen/Zotero/storage/Q7DZ4WXX/Bartonitz et al. - 2018 - Agile Verwaltung Wie der Öffentliche Dienst aus d.pdf:application/pdf},
}

@phdthesis{hanisch_wirkgefuge_2022,
	address = {Hürth},
	type = {Exposee},
	title = {Wirkgefüge im digitalem {Bildungsraum}: {Eine} {Untersuchung} der {Merkmale}, {Effekte}, {Mechanismen} und {Reaktionen} von {Learning}-{Management}-{Systemen} am {Beispiel} der {Lehre} in {Gesundheitsberufen}},
	copyright = {Creative Commons Namensnennung - Nicht kommerziell - Keine Bearbeitungen 4.0 Internationale Lizenz (CC-BY-NC-ND)},
	url = {https://www.researchgate.net/publication/373216529_Hanisch_J_2022_Wirkgefuge_im_digitalem_Bildungsraum_Eine_Untersuchung_der_Merkmale_Effekte_Mechanismen_und_Reaktionen_von_Learning-Management-Systemen_am_Beispiel_der_Lehre_in_Gesundheitsberufen_Expos},
	abstract = {Die Ausbildung im Rettungsdienst wurde durch das Gesetz über den Beruf der Notfallsanitäterin und des Notfallsanitäters (NotSanG) zum 01.01.2014 von einer zweijährigen zu einer dreijährigen Qualifikation geändert (§ 5 (1) NotSanG, 2021). Das Bildungszentrum (BZ) des Deutschen Roten Kreuzes (DRK), Kreisverband Düsseldorf e.V. führt die Ausbildung von Notfallsanitäterinnen und Notfallsanitätern (NFS, NotSan) seit Beginn des ersten Jahrgangs im März 2017 durch. Der Gesetzgeber transformierte die Tätigkeit weg von einem Assistenzberuf hin zu einer beruflichen Qualifikation, in deren Mittelpunkt das kompetenzorientierte sowie selbstverantwortliche Handeln gerückt wurde (NotSanG, 2021, § 4). Um dem Ausbildungsziel gerecht zu werden, ist in die Ausbildung der NFS die Verwendung eines digitalen Learning-Management-Systems (LMS) vorgesehen.},
	language = {de-DE},
	school = {Charité – Universitätsmedizin Berlin},
	author = {Hanisch, Jochen},
	month = mar,
	year = {2022},
	keywords = {Bildung, Multimedia, Promotion:Literaturanalyse, Charité:Promotion},
	file = {Hanisch - 2022 - Wirkgefüge im digitalem Bildungsraum Eine Untersu.pdf:/Users/jochenhanisch-johannsen/Zotero/storage/V6UXGLF2/Hanisch - 2022 - Wirkgefüge im digitalem Bildungsraum Eine Untersu.pdf:application/pdf},
}

@book{adam_agil_2020,
	address = {Wiesbaden},
	series = {essentials},
	title = {Agil in der {ISO} 9001: {Wie} {Sie} agile {Prozesse} in {Ihr} {Qualitätsmanagement} integrieren},
	isbn = {978-3-658-28310-0 978-3-658-28311-7},
	shorttitle = {Agil in der {ISO} 9001},
	url = {http://link.springer.com/10.1007/978-3-658-28311-7},
	language = {de},
	urldate = {2022-08-20},
	publisher = {Springer Fachmedien Wiesbaden},
	author = {Adam, Patricia},
	year = {2020},
	doi = {10.1007/978-3-658-28311-7},
	keywords = {Bildung, Multimedia, FernUni-Hagen:MABM:Master-Arbeit, Promotion:Literaturanalyse, Charité:Promotion},
	file = {Adam - 2020 - Agil in der ISO 9001 Wie Sie agile Prozesse in Ih.pdf:/Users/jochenhanisch-johannsen/Zotero/storage/73WTJUEX/Adam - 2020 - Agil in der ISO 9001 Wie Sie agile Prozesse in Ih.pdf:application/pdf;Adam - 2020 - Agil in der ISO 9001 Wie Sie agile Prozesse in Ih.pdf:/Users/jochenhanisch-johannsen/Zotero/storage/IQA47G7U/Adam - 2020 - Agil in der ISO 9001 Wie Sie agile Prozesse in Ih.pdf:application/pdf},
}

@phdthesis{diegel_33091-01_nodate,
	title = {33091-01 - {E}-{Portfolio}: {Kompetenzerwerb} und {Digitalisierung} für das {Lehren} und {Lernen}},
	language = {de},
	author = {Diegel, Noëlle and Weidlich, Joshua and Deimann, Markus},
	keywords = {Bildung, Charité:Promotion, E-Portfolio, Multimedia, Promotion:Literaturanalyse},
	file = {Diegel et al. - 33091-01 - E-Portfolio Kompetenzerwerb und Digita.pdf:/Users/jochenhanisch-johannsen/Zotero/storage/ACIBMZR6/Diegel et al. - 33091-01 - E-Portfolio Kompetenzerwerb und Digita.pdf:application/pdf},
}

@phdthesis{bastiaens_gestaltung_2017,
	address = {Hagen},
	type = {Studienbrief},
	title = {Gestaltung und {Entwicklung} von neuen {Medien}},
	copyright = {Alle Rechte vorbehalten (Urheberrechtlich geschützt. Persönliche Kopie für Matrikelnummer 8135649)},
	school = {FernUniversität in Hagen, Fakultät für Kultur- und Sozialwissenschaften},
	author = {Bastiaens, Theo J.},
	year = {2017},
	keywords = {Bildung, Multimedia, FernUni-Hagen, Promotion:Literaturanalyse, Charité:Promotion},
	file = {bastiaens_2017_gestaltung_und_entwicklung_von_neuen_medien.pdf:/Users/jochenhanisch-johannsen/Zotero/storage/W2E4HMZQ/bastiaens_2017_gestaltung_und_entwicklung_von_neuen_medien.pdf:application/pdf},
}

@inproceedings{drachsler_privacy_2016,
	address = {Edinburgh, United Kingdom},
	title = {Privacy and analytics: it's a {DELICATE} issue a checklist for trusted learning analytics},
	isbn = {978-1-4503-4190-5},
	shorttitle = {Privacy and analytics},
	url = {10.1145/2883851.2883893},
	doi = {10/ggcj7v},
	abstract = {Künstliche Intelligenz (KI) wird zukünftig weltweit die Entwicklung von Hochschulen signifikant prägen (bspw. Aldosari, 2020). Auch in Deutschland wächst das hochschulpolitische Interesse, KI in die didaktische Gestaltung von Bildungseinrichtungen einzubinden und zu fördern (Bundesregierung, 2020; Kieslich et al., 2019). Dieser prognostizierte Trend sowie der bildungspolitische Wunsch nach KI in der Hochschullehre bildet derzeit vielfältige Anlässe für teils umfangreiche Forschungs- und Entwicklungsbemühungen. Während im technologischen, bildungswissenschaftlichen und didaktischen Bereich bereits Forschungsergebnisse und auch erste Implementationen vorliegen, ist die Akzeptanzforschung vor allem aus Studierendenperspektive gegenüber KI-Anwendungen vergleichsweise wenig entwickelt.},
	language = {de},
	urldate = {2021-03-09},
	booktitle = {Proceedings of the {Sixth} {International} {Conference} on {Learning} {Analytics} \& {Knowledge} - {LAK} '16},
	publisher = {ACM Press},
	author = {Drachsler, Hendrik and Greller, Wolfgang},
	year = {2016},
	note = {ZSCC: 0000245},
	keywords = {Promotion:Literaturanalyse, Charité:Promotion, Leraning:Analytics},
	pages = {89--98},
	file = {Abstract_WatanabeSchmohl_v2.pdf:/Users/jochenhanisch-johannsen/Zotero/storage/LRN3ZT3T/Abstract_WatanabeSchmohl_v2.pdf:application/pdf},
}

@techreport{godbole_ai_nodate,
	title = {Ai \& {Machine} {Learning} for the {Indian} {Navy}},
	abstract = {Die indische Marine ist eine technologieerfahrene Truppe. Die Plattformen der neuen Generation, die sie betreibt, sind mit modernster Technologie ausgestattet. Dies versetzt sie in eine vorteilhafte Position, um neue KI-Technologien zu entwickeln und aufzunehmen, die beim Militär und in der Industrie immer beliebter werden.
Die indische Regierung hat bereits konkrete Schritte in diese Richtung unternommen. Nachdem sie die potenziellen sektorübergreifenden Auswirkungen der künstlichen Intelligenz (KI)/des maschinellen Lernens (ML) identifiziert hatte, beauftragte sie 2018 die NITI Aayog und das Verteidigungsministerium mit der Aufstellung eines Fahrplans für die Ausarbeitung eines nationalen Programms, das auf die Forschung und Entwicklung (F\&E) von KI-Anwendungen im sozialen Sektor bzw. in den Streitkräften abzielt. Folglich veröffentlichte NITI Aayog im Juni 2018 ein Weißbuch mit dem Titel "Nationale Strategie für KI", während die vom Verteidigungsministerium eingesetzte Task Force "Strategische Umsetzung der KI für nationale Sicherheit und Verteidigung" ihre Empfehlungen vorlegte.
Die Task Force identifizierte Anwendungsfälle, die von strategischem Wert sind, aber längere Forschungs- und Entwicklungszyklen beinhalten. Die langfristigen Ziele der indischen Marine, sich bis 2027 in eine 200-Schiff-Truppe umzuwandeln, und ihre anhaltenden Impulse zur Aufrechterhaltung einer optimalen Kampffähigkeit werden immer wieder durch die abnehmende Verfügbarkeit von Kapital und den Mangel an Arbeitskräften auf die Probe gestellt. Es ist daher unerlässlich, dass die Streitkräfte und mehr noch die indische Marine die Vorteile der AI/ML-basierten Technologien zur Verbesserung der organisatorischen Effizienz auf verschiedenen Ebenen nutzen.
Dieses Papier konzentriert sich auf vier weitere Anwendungsfälle, nämlich Bestandsmanagement, Ausbildung, präskriptive Wartung sowie Sicherheit und Überwachung, die in der indischen Marine umgesetzt werden sollen. Die zusätzlich identifizierten Anwendungsfälle basieren auf etablierten Industriekapazitäten und haben daher kürzere F\&E-Zyklen. Diese Lösungen können, sobald sie demonstriert wurden, leicht auf die beiden anderen Streitkräfte hochskaliert werden. Die Beteiligung der Industrie an der Förderung der Entwicklung von Fähigkeiten und der Bereitstellung von Machbarkeitsnachweisen wird für die Entstehung kommerziell rentabler und nachhaltiger Technologien von entscheidender Bedeutung sein.
Ein kohärenter Drei-Dienste-Ansatz wird notwendig sein, um die Interoperabilität und die optimale Nutzung der knappen Ressourcen bei der Entwicklung skalierbarer KI/ML-Lösungen aufrechtzuerhalten. Das Department of Military Affairs (DMA) unter der Leitung des Chief of Defence Staff (CDS) kann die Entwicklung einer gemeinsamen Tri-Services-Strategie und einer interoperablen Infrastruktur initiieren. Die Einbeziehung des CDS und geeigneter Vertreter der DMA in den Defence Al Council (DAIC) bzw. die Defence Al Project Agency (DAIPA) wird wesentlich dazu beitragen, einen synergistischen Ansatz für die Absorption von KI-basierten Technologien zu entwickeln.
Die in diesem Papier identifizierten KI-Anwendungsfälle haben auch kommerzielle Anwendungen. Die Entwicklung dieser KI-Technologien in Zusammenarbeit mit der Industrie und der akademischen Welt wird dazu beitragen, den derzeitigen Trend in Industriekreisen umzukehren, die Technologie zunächst für die kommerzielle Nutzung zu entwickeln und sie dann in geeigneter Weise für militärische Anwendungen zu modifizieren. Übersetzt mit www.DeepL.com/Translator (kostenlose Version)},
	language = {en},
	author = {Godbole, Cdr Amrut},
	keywords = {Promotion:Literaturanalyse, Charité:Promotion, Promotion:Literaturanalyse:Berichte},
	pages = {18},
	file = {AI-Machine-Learning-paper-for-the-Indian-Navy_Cdr-Godbole_Final-Version_GH-1.pdf:/Users/jochenhanisch-johannsen/Zotero/storage/AC75IU6Q/AI-Machine-Learning-paper-for-the-Indian-Navy_Cdr-Godbole_Final-Version_GH-1.pdf:application/pdf},
}

@phdthesis{schuh_forschendes_nodate,
	title = {Forschendes {Lernen} in der {Hochschullehre} - {Chancen} und {Hindernisse}},
	abstract = {Since the Bologna reform inquiry-based learning is a widely discussed concept to better interlock higher education with research again so that education quality improves. Using expert interviews as a qualitative method, this thesis analyses factors with fostering or hindering effects on the implementation of inquirybased learning from the viewpoint of university instructors. This shall be done by the analysis of research literature as a basis for the identiﬁcation of appropriate factors, which are subsequently veriﬁed and complemented with the help of the interviews. The results acquired in this way are inputted into a conception of an online seminar, where the use of digital media is geared to the needs of inquiry-based learning.},
	language = {de},
	author = {Schuh, Daniela},
	keywords = {Promotion:Literaturanalyse, Charité:Promotion},
	file = {schuh_forschendes_lernen_in_der_hochschullehre_-_chancen_und_hindernisse.pdf:/Users/jochenhanisch-johannsen/Zotero/storage/K64SXVYC/schuh_forschendes_lernen_in_der_hochschullehre_-_chancen_und_hindernisse.pdf:application/pdf},
}

@incollection{steinbrecher_agiles_2020,
	address = {Berlin, Heidelberg},
	title = {Agiles {Projektmanagement} nach {Scrum}: {Ein} {Modell} in drei {Phasen}},
	isbn = {978-3-662-59704-0 978-3-662-59705-7},
	shorttitle = {Agiles {Projektmanagement} nach {Scrum}},
	url = {http://link.springer.com/10.1007/978-3-662-59705-7_6},
	language = {de},
	urldate = {2021-07-18},
	booktitle = {Agile {Einführung} der {E}-{Akte} mit {Scrum}},
	publisher = {Springer Berlin Heidelberg},
	author = {Fischbach, Jan and Steinbrecher, Wolf},
	editor = {Steinbrecher, Wolf},
	year = {2020},
	doi = {10.1007/978-3-662-59705-7_6},
	keywords = {Agilität, Scrum, FernUni-Hagen:MABM:Master-Arbeit, Promotion:Literaturanalyse, Charité:Promotion},
	pages = {117--137},
	file = {Fischbach und Steinbrecher - 2020 - Agiles Projektmanagement nach Scrum Ein Modell in.pdf:/Users/jochenhanisch-johannsen/Zotero/storage/2H48GCS3/Fischbach und Steinbrecher - 2020 - Agiles Projektmanagement nach Scrum Ein Modell in.pdf:application/pdf},
}

@article{nichol_typology_2021,
	title = {A {Typology} of {Existing} {Machine} {Learning}?{Based} {Predictive} {Analytic} {Tools} {Focused} on {Reducing} {Costs} and {Improving} {Quality} in {Health} {Care}: {Systematic} {Search} and {Content} {Analysis}},
	volume = {23},
	url = {https://www.jmir.org/2021/6/e26391},
	doi = {10.2196/26391},
	abstract = {Background: Considerable effort has been devoted to the development of artificial intelligence, including machine learning?based predictive analytics (MLPA) for use in health care settings. The growth of MLPA could be fueled by payment reforms that hold health care organizations responsible for providing high-quality, cost-effective care. Policy analysts, ethicists, and computer scientists have identified unique ethical and regulatory challenges from the use of MLPA in health care. However, little is known about the types of MLPA health care products available on the market today or their stated goals. Objective: This study aims to better characterize available MLPA health care products, identifying and characterizing claims about products recently or currently in use in US health care settings that are marketed as tools to improve health care efficiency by improving quality of care while reducing costs. Methods: We conducted systematic database searches of relevant business news and academic research to identify MLPA products for health care efficiency meeting our inclusion and exclusion criteria. We used content analysis to generate MLPA product categories and characterize the organizations marketing the products. Results: We identified 106 products and characterized them based on publicly available information in terms of the types of predictions made and the size, type, and clinical training of the leadership of the companies marketing them. We identified 5 categories of predictions made by MLPA products based on publicly available product marketing materials: disease onset and progression, treatment, cost and utilization, admissions and readmissions, and decompensation and adverse events. Conclusions: Our findings provide a foundational reference to inform the analysis of specific ethical and regulatory challenges arising from the use of MLPA to improve health care efficiency.},
	number = {6},
	journal = {J Med Internet Res},
	author = {Nichol, A. Ariadne and Batten, N. Jason and Halley, C. Meghan and Axelrod, K. Julia and Sankar, L. Pamela and Cho, K. Mildred},
	month = jun,
	year = {2021},
	keywords = {Ethik, artificial intelligence, machine learning, costs, health care quality, regulation, Promotion:Literaturanalyse, Charité:Promotion, Leraning:Analytics},
	pages = {e26391},
}

@techreport{schultz-pernice_digitales_nodate,
	title = {Digitales {Lehren} und {Lernen} an der {Hochschule}: {Erkenntnisse} aus der empirischen {Lehr}-{Lernforschung}},
	language = {de},
	author = {Schultz-Pernice, Florian and Becker, Sabine and Berger, Sonja and Ploch, Nina and Radkowitsch, Anika and Vejvoda, Johanna and Fischer, Frank},
	keywords = {Promotion:Literaturanalyse, Charité:Promotion, Promotion:Literaturanalyse:Berichte},
	pages = {17},
	file = {Schultz-Pernice et al. - Digitales Lehren und Lernen an der Hochschule Erk.pdf:/Users/jochenhanisch-johannsen/Zotero/storage/RM797SIG/Schultz-Pernice et al. - Digitales Lehren und Lernen an der Hochschule Erk.pdf:application/pdf},
}

@techreport{redecker_digitale_2019,
	address = {Brussels},
	type = {Studie},
	title = {Digitale {Kompetenz} {Lehrender}},
	url = {https://joint-research-centre.ec.europa.eu/system/files/2019-09/digcompedu_german_final.pdf},
	abstract = {Da Lehrende sich schnell ändernden Anforderungen gegenübersehen, benötigen sie ein immer breiteres und differenzierteres Spektrum an Kompetenzen als je zuvor. Insbesondere die Allgegenwart digitaler Geräte und die Verpflichtung, Schülerinnen und Schülern zu digitaler Kompetenz zu verhelfen, erfordern, dass Lehrende ihre eigene digitale Kompetenz entwickeln.
Auf internationaler und nationaler Ebene wurden eine Reihe von Referenzrahmen, Selbsteinschätzungsinstrumenten und Schulungsprogrammen entwickelt, um die Facetten der digitalen Kompetenz für Lehrende zu beschreiben und ihnen dabei zu helfen, ihre Kompetenz einzuschätzen, ihren Schulungsbedarf zu ermitteln und gezielte Schulungen anzubieten. Basierend auf der Analyse und dem Vergleich dieser Instrumente präsentiert dieser Bericht einen gemeinsamen Europäischen Rahmen für die digitale Kompetenz Lehrender (DigCompEdu). DigCompEdu ist ein wissenschaftlich fundierter Referenzrahmen, der als Orientierungshilfe dient und direkt an die Implementierung regionaler und nationaler Tools und Schulungsprogramme angepasst werden kann. Darüber hinaus werden eine gemeinsame Sprache und ein gemeinsamer Ansatz bereitgestellt, die den grenzüberschreitenden Dialog und Austausch bewährter Verfahren erleichtern.
Der Europäische Rahmen für die digitale Kompetenz Lehrender richtet sich an Lehrende auf allen Bildungsebenen, von der frühen Kindheit bis zur Hochschul- und Erwachsenenbildung, einschließlich allgemeiner und beruflicher Bildung und Ausbildung, Sonderpädagogik und nicht formaler Lernkontexte. Ziel ist es, einen allgemeinen Bezugsrahmen für Entwickler digitaler Kompetenzmodelle bereitzustellen, d.h. Für Mitgliedstaaten, Regionalregierungen, relevante nationale und regionale Agenturen, Bildungsorganisationen selbst sowie öffentliche oder private Anbieter von Berufsausbildungen.},
	language = {de},
	urldate = {2022-03-12},
	institution = {Gemeinsame Forschungsstelle der Europäischen Kommission, Europäische Union},
	author = {Redecker, Christine and Punie, Yves},
	translator = {{Goethe-Institut e.V.}},
	year = {2019},
	keywords = {FernUni-Hagen:MABM:M6, Promotion:Literaturanalyse, Charité:Promotion, Promotion:Literaturanalyse:Berichte},
	pages = {60},
	file = {Redecker und Punie - 2019 - Digitale Kompetenz Lehrender.pdf:/Users/jochenhanisch-johannsen/Zotero/storage/83S9A9TJ/Redecker und Punie - 2019 - Digitale Kompetenz Lehrender.pdf:application/pdf},
}

@phdthesis{faschingbauer_digitale_2022,
	title = {Digitale {Medien} und soziale {Anerkennung}. {Welchen} {Einfluss} hat die {Mediennutzung} auf das {Selbstbild} junger {Erwachsener}, {Cybermobbing} und den zwischenmenschlichen {Umgang} in der {Gesellschaft}? {Analyse} am {Beispiel} der {Sci}-{Fi} {Serie} "{Black} {Mirror}“},
	language = {de},
	author = {Faschingbauer, Franziska},
	month = apr,
	year = {2022},
	keywords = {Promotion:todo, Promotion:Literaturanalyse, Charité:Promotion},
	file = {Faschingbauer - Digitale Medien und soziale Anerkennung..pdf:/Users/jochenhanisch-johannsen/Zotero/storage/ZHHBPATL/Faschingbauer - Digitale Medien und soziale Anerkennung..pdf:application/pdf},
}

@techreport{rohs_digitalisierung_2022,
	address = {Kaiserslautern},
	type = {Seminarbericht},
	title = {Digitalisierung in der {Erwachsenenbildung} in Österreich},
	abstract = {Der Bericht umfasst sechs Beiträge von Studierende der Karl-Franzenz-Universität Graz, die sich im Wintersemester 2021/2021 mit Fragen der Digitalisierung der Erwachsenenbildung in Österreich auseinandergesetzt haben. In diesem Rahmen wurden die Themenfelder Lernen und Lehren mit digitalen Medien, die medienpädagogische Professionalisierung von Erwachsenenbilnder:innen, die Digitalisierung der Anbieter sowie die Digitalisierung als Inhalt in den Angeboten und der (Weiter)Bildungspolitik beleuchtet. Dabei wurde zum einen vorliegende Forschungsarbeiten ausgewertet, also auch ergänzende Recherchen auf durchgeführt, um die Datengrundlage zu verbessern. Allgemein wurde deutlich, dass die Forschungslage noch keine befriedigenden Rückschlüsse auf den Digitalisierungsprozess und dessen Wirkungen auf die Erwachsenenbildung/Weiterbildung in Österreich zulassen. Insofern stellt die hier vorliegende Zusammenstellen ein wichtigen Beitrag dar, die vorliegenden Desiderata zu identifizieren.},
	language = {de-DE},
	number = {12},
	institution = {Technische Universität Kaiserslautern {\textbar} Fachgebiet Pädagogik},
	author = {Rohs, Matthias and Gruber, Elke},
	month = apr,
	year = {2022},
	keywords = {Promotion:todo, Promotion:Literaturanalyse, Charité:Promotion, Promotion:Literaturanalyse:Berichte},
	pages = {51},
	file = {Rohs und Gruber - Digitalisierung in der Erwachsenenbildung in Öster.pdf:/Users/jochenhanisch-johannsen/Zotero/storage/L5GCRJMW/Rohs und Gruber - Digitalisierung in der Erwachsenenbildung in Öster.pdf:application/pdf},
}

@phdthesis{durgerian_competence-based_2022,
	title = {A {Competence}-{Based} {Online} {Learning} {Video} and {In}-{Situ} {Simulation} to {Improve} {Perioperative} {Anesthesia} {Nurse} {Practitioner} {Self}-{Efficacy} in {Responding} to {Anesthesia} {Emergencies}},
	abstract = {Background: Nurse Practitioners (NPs) are broadly educated to the population-based role in which they practice. Further education in subspecialties is essential as more NPs are working autonomously in highly specialized care areas. Problem: In the Department of Anesthesia at a large urban hospital, perioperative anesthesia NPs lack formal training in the subspecialty of anesthesia, which contributed to a lack of self-efficacy when responding to anesthesia emergencies.
Methods: An asynchronous multimodal brief instructional video accompanied by an in-situ simulation of an anesthesia emergency was developed to increase knowledge and confidence in perioperative anesthesia nurse practitioner response to anesthesia emergencies.
Results: A total of 8 perioperative anesthesia NPs (73\% of the staff) participated in the multimodal educational intervention, and 100\% of the participants experienced an increase in knowledge to locate emergency anesthesia equipment, along with increased confidence levels in responding to an anesthesia emergency scenario after watching the video and performing the insitu simulation.
Conclusion: Deploying a multimodal educational video along with an in-situ simulation was effective in increasing participant’s self-efficacy when responding to an anesthesia emergency, and was found to be feasible. Inadequate educational resources, poor inclusivity of the NPs in the culture of education, and limited time allotted for education were addressed by providing open access of the video on the internet. In-situ simulation reinforced education through a realistic hands-on scenario and provided repetition with the use of Rapid Cycle Deliberate Practice.},
	language = {en},
	school = {UniversityofMassachusettsBoston},
	author = {Durgerian, Sara},
	month = may,
	year = {2022},
	keywords = {Bildung, Multimedia, Promotion:Literaturanalyse, Charité:Promotion},
	file = {Durgerian - A Competence-Based Online Learning Video and In-Si.pdf:/Users/jochenhanisch-johannsen/Zotero/storage/UGZI4TH8/Durgerian - A Competence-Based Online Learning Video and In-Si.pdf:application/pdf},
}

@book{muller_entwicklung_2022,
	address = {Wiesbaden},
	series = {{BestMasters}},
	title = {Entwicklung eines {High} {Performance} {Learning} {Journey} {Konzepts} zur organisationalen {Weiterbildung} agiler {Rollen} im {Rahmen} von {SAFe}: {Spezifiziert} am {Beispiel} des {Scrum} {Masters}},
	isbn = {978-3-658-36868-5 978-3-658-36869-2},
	shorttitle = {Entwicklung eines {High} {Performance} {Learning} {Journey} {Konzepts} zur organisationalen {Weiterbildung} agiler {Rollen} im {Rahmen} von {SAFe}},
	url = {https://link.springer.com/10.1007/978-3-658-36869-2},
	language = {de},
	urldate = {2022-07-08},
	publisher = {Springer Fachmedien Wiesbaden},
	author = {Müller, Keven},
	year = {2022},
	doi = {10.1007/978-3-658-36869-2},
	keywords = {Bildung, Multimedia, FernUni-Hagen:MABM:Master-Arbeit, Promotion:Literaturanalyse, Charité:Promotion},
	file = {Müller - 2022 - Entwicklung eines High Performance Learning Journe.pdf:/Users/jochenhanisch-johannsen/Zotero/storage/L5L3B8ES/Müller - 2022 - Entwicklung eines High Performance Learning Journe.pdf:application/pdf},
}

@incollection{longmus_agiles_2021,
	address = {Berlin, Heidelberg},
	title = {Agiles {Lernen} im {Unternehmen}: {Prinzipien}, {Ablauf}, {Rollen}, {Instrumente}},
	isbn = {978-3-662-62012-0 978-3-662-62013-7},
	shorttitle = {Agiles {Lernen} im {Unternehmen}},
	url = {http://link.springer.com/10.1007/978-3-662-62013-7_2},
	language = {de},
	urldate = {2022-07-08},
	booktitle = {Agiles {Lernen} im {Unternehmen}},
	publisher = {Springer Berlin Heidelberg},
	author = {Korge, Gabriele and Höhne, Benjamin and Bauer, Agnes and Longmuß, Jörg},
	editor = {Longmuß, Jörg and Korge, Gabriele and Bauer, Agnes and Höhne, Benjamin},
	year = {2021},
	doi = {10.1007/978-3-662-62013-7_2},
	keywords = {FernUni-Hagen:MABM:Master-Arbeit, Promotion:Literaturanalyse, Charité:Promotion},
	pages = {9--19},
	file = {Korge et al. - 2021 - Agiles Lernen im Unternehmen Prinzipien, Ablauf, .pdf:/Users/jochenhanisch-johannsen/Zotero/storage/PI5LMFXS/Korge et al. - 2021 - Agiles Lernen im Unternehmen Prinzipien, Ablauf, .pdf:application/pdf},
}

@book{ackermann_erfolgreicher_2020,
	address = {Wiesbaden},
	title = {Erfolgreicher {Wissenstransfer} in agilen {Organisationen}: {Hintergrund} – {Methodik} – {Praxisbeispiele}},
	isbn = {978-3-658-31874-1 978-3-658-31875-8},
	shorttitle = {Erfolgreicher {Wissenstransfer} in agilen {Organisationen}},
	url = {https://link.springer.com/10.1007/978-3-658-31875-8},
	language = {de},
	urldate = {2022-07-08},
	publisher = {Springer Fachmedien Wiesbaden},
	author = {Ackermann, Benno and Krancher, Oliver and North, Klaus and Schildknecht, Katrin and Schorta, Silvia},
	year = {2020},
	doi = {10.1007/978-3-658-31875-8},
	keywords = {Bildung, Multimedia, FernUni-Hagen:MABM:Master-Arbeit, Promotion:Literaturanalyse, Charité:Promotion},
	file = {Ackermann et al. - 2020 - Erfolgreicher Wissenstransfer in agilen Organisati.pdf:/Users/jochenhanisch-johannsen/Zotero/storage/PI6FNDHD/Ackermann et al. - 2020 - Erfolgreicher Wissenstransfer in agilen Organisati.pdf:application/pdf},
}

@book{karlshaus_agiles_2021,
	address = {Berlin, Heidelberg},
	title = {Agiles {Human} {Resources}: {Kundenzentriertes} {Denken} und {Handeln} im {Personalbereich}},
	isbn = {978-3-662-63537-7 978-3-662-63538-4},
	shorttitle = {Agiles {Human} {Resources}},
	url = {https://link.springer.com/10.1007/978-3-662-63538-4},
	language = {de},
	urldate = {2022-07-08},
	publisher = {Springer Berlin Heidelberg},
	editor = {Karlshaus, Anja and Wolf, Anke},
	year = {2021},
	doi = {10.1007/978-3-662-63538-4},
	keywords = {Bildung, Multimedia, FernUni-Hagen:MABM:Master-Arbeit, Promotion:Literaturanalyse, Charité:Promotion},
	file = {Karlshaus und Wolf - 2021 - Agiles Human Resources Kundenzentriertes Denken u.pdf:/Users/jochenhanisch-johannsen/Zotero/storage/H24NTBUA/Karlshaus und Wolf - 2021 - Agiles Human Resources Kundenzentriertes Denken u.pdf:application/pdf},
}

@book{longmus_agiles_2021-1,
	address = {Berlin, Heidelberg},
	title = {Agiles {Lernen} im {Unternehmen}},
	isbn = {978-3-662-62012-0 978-3-662-62013-7},
	url = {http://link.springer.com/10.1007/978-3-662-62013-7},
	language = {de},
	urldate = {2022-07-08},
	publisher = {Springer Berlin Heidelberg},
	editor = {Longmuß, Jörg and Korge, Gabriele and Bauer, Agnes and Höhne, Benjamin},
	year = {2021},
	doi = {10.1007/978-3-662-62013-7},
	keywords = {Bildung, Multimedia, Agilität, FernUni-Hagen:MABM:Master-Arbeit, Promotion:Literaturanalyse, Charité:Promotion},
	file = {Longmuß et al. - 2021 - Agiles Lernen im Unternehmen.pdf:/Users/jochenhanisch-johannsen/Zotero/storage/T5KRLKS4/Longmuß et al. - 2021 - Agiles Lernen im Unternehmen.pdf:application/pdf},
}

@book{steinbrecher_agile_2020,
	address = {Berlin, Heidelberg},
	title = {Agile {Einführung} der {E}-{Akte} mit {Scrum}: {Die} digitale {Akte} als kollaborative {Teamplattform} aufsetzen},
	isbn = {978-3-662-59704-0 978-3-662-59705-7},
	shorttitle = {Agile {Einführung} der {E}-{Akte} mit {Scrum}},
	url = {http://link.springer.com/10.1007/978-3-662-59705-7},
	language = {de},
	urldate = {2021-07-18},
	publisher = {Springer Berlin Heidelberg},
	editor = {Steinbrecher, Wolf},
	year = {2020},
	doi = {10.1007/978-3-662-59705-7},
	keywords = {Bildung, Multimedia, FernUni-Hagen:MABM:Master-Arbeit, Promotion:Literaturanalyse, Charité:Promotion},
	file = {Steinbrecher - 2020 - Agile Einführung der E-Akte mit Scrum Die digital.pdf:/Users/jochenhanisch-johannsen/Zotero/storage/6MN5Y5Z5/Steinbrecher - 2020 - Agile Einführung der E-Akte mit Scrum Die digital.pdf:application/pdf},
}

@book{sauter_agile_2018,
	address = {Berlin, Heidelberg},
	title = {Agile {Werte}- und {Kompetenzentwicklung}},
	isbn = {978-3-662-57304-4 978-3-662-57305-1},
	url = {http://link.springer.com/10.1007/978-3-662-57305-1},
	language = {de},
	urldate = {2022-06-22},
	publisher = {Springer Berlin Heidelberg},
	author = {Sauter, Roman and Sauter, Werner and Wolfig, Roland},
	year = {2018},
	doi = {10.1007/978-3-662-57305-1},
	keywords = {Bildung, Multimedia, FernUni-Hagen:MABM:Master-Arbeit, Promotion:Literaturanalyse, Charité:Promotion},
	file = {Sauter et al. - 2018 - Agile Werte- und Kompetenzentwicklung.pdf:/Users/jochenhanisch-johannsen/Zotero/storage/8GCYXCCB/Sauter et al. - 2018 - Agile Werte- und Kompetenzentwicklung.pdf:application/pdf},
}

@incollection{de_witt_mobile_2018,
	address = {Wiesbaden},
	title = {Mobile {Learning} {Analytics}: {Potenziale} für {Lernen} und {Lehren} am {Beispiel} {Hochschule}},
	isbn = {978-3-658-19122-1 978-3-658-19123-8},
	shorttitle = {Mobile {Learning} {Analytics}},
	url = {http://link.springer.com/10.1007/978-3-658-19123-8_29},
	language = {de},
	urldate = {2022-08-20},
	booktitle = {Handbuch {Mobile} {Learning}},
	publisher = {Springer Fachmedien Wiesbaden},
	author = {Seiler, Luisa and Kuhnel, Matthias and Honal, Andrea and Ifenthaler, Dirk},
	editor = {de Witt, Claudia and Gloerfeld, Christina},
	year = {2018},
	doi = {10.1007/978-3-658-19123-8_29},
	keywords = {Promotion:Literaturanalyse, Charité:Promotion, Leraning:Analytics},
	pages = {585--608},
	file = {Seiler et al. - 2018 - Mobile Learning Analytics Potenziale für Lernen u.pdf:/Users/jochenhanisch-johannsen/Zotero/storage/NQAWNYC7/Seiler et al. - 2018 - Mobile Learning Analytics Potenziale für Lernen u.pdf:application/pdf},
}

@book{hasebrook_wie_2019,
	address = {Wiesbaden},
	series = {essentials},
	title = {Wie {Organisationen} erfolgreich agil werden: {Hinweise} zur erfolgreichen {Umsetzung} in {Zusammenarbeit} und {Strategie}},
	isbn = {978-3-658-26809-1 978-3-658-26810-7},
	shorttitle = {Wie {Organisationen} erfolgreich agil werden},
	url = {http://link.springer.com/10.1007/978-3-658-26810-7},
	language = {de},
	urldate = {2022-08-20},
	publisher = {Springer Fachmedien Wiesbaden},
	author = {Hasebrook, Joachim and Kirmße, Stefan and Fürst, Martin},
	year = {2019},
	doi = {10.1007/978-3-658-26810-7},
	keywords = {Bildung, Multimedia, FernUni-Hagen:MABM:Master-Arbeit, Promotion:Literaturanalyse, Charité:Promotion},
	file = {Hasebrook et al. - 2019 - Wie Organisationen erfolgreich agil werden Hinwei.pdf:/Users/jochenhanisch-johannsen/Zotero/storage/EMV5S4EA/Hasebrook et al. - 2019 - Wie Organisationen erfolgreich agil werden Hinwei.pdf:application/pdf},
}

@techreport{henning_delfi_2022,
	address = {Karlsruhe},
	type = {Konferenzbericht},
	title = {{DELFI} 2022: 20. {Fachtagung} {Bildungstechnologien} der {Gesellschaft} für {Informatik} e.{V}},
	abstract = {Die DELFI-Tagungsreihe der Gesellschaft für Informatik befasst sich interdisziplinär mit Bildungstechnologien und spannt dabei den Bogen von der praktischen Anwendung digitaler Werkzeuge bis zu den neuesten Forschungsergebnissen. Die 20. Fachtagung Bildungstechnologien im Jahr 2022 beschäftigt sich mit allen Informatik-Aspekten von digital unterstützten Lehr- und Lernformen in Schule, Hochschule, beruflicher und privater Aus- und Weiterbildung. Der Forschungsaspekt adressiert insbesondere Technologien, Werkzeuge, Infrastrukturen und organisatorische, soziale und technische Rahmenbedingungen für digitale Bildung, unabhängig von konkreten Anwendungsfeldern und Lerninhalten – insbesondere also auch außerhalb der Informatikfächer. Während der Corona-Krise wurden diese Technologien auf vorher nicht antizipierte Weise auch bei solchen öffentlichen, kommerziellen und privaten Bildungsangeboten in den Fokus katapultiert, die bislang eher auf traditionelle Lehr- und Lernformen gesetzt hatten. Damit aber stellt sich die Frage, ob dies nur eine vorübergehende Krisenreaktion war, oder ob sich mindestens einige Arten des technologiegestützten Lernens auch nachhaltig etablieren werden. Das hängt einerseits von einer Verstetigung von Projekten und Fördermaßnahmen ab – und setzt natürlich andererseits voraus, dass sich auf digitalem Weg Bildung nachweisbar mindestens gleich gut wie mit den genannten traditionellen Methoden vermitteln lässt. Das Motto der 20. Fachtagung Bildungstechnologien lautet deshalb „Digitale Lehre nachhaltig gestalten“ und wir haben in diesem Jahr insbesondere Beiträge ermutigt, die dieses Nachhaltigkeitsthema adressieren. Gleichzeitig wollen wir wieder brandaktuelle Themen aufgreifen, die auch die Bildungstechnologie umwälzen werden: Künstliche Intelligenz (KI) und natürlichsprachliche Schnittstellen sind die entsprechenden Stichworte. Die eingereichten wissenschaftlichen Beiträge wurden im Doppelblind-Verfahren von je mindestens drei Mitgliedern des Programmkomitees begutachtet. Bei einer Programmkomitee-Sitzung wurde dann über die Annahme und Ablehnung von Beiträgen entschieden. Eingereicht wurden 23 Langbeiträge, von denen 6 angenommen wurden (Annahmequote 26\%) sowie 18 Kurzbeiträge, von denen 5 angenommen wurden (Annahmequote 28\%). Zusammen mit einigen gekürzten Langbeiträgen (und abzüglich zweier leider zurückgezogener Beiträge) werden insgesamt 8 Kurzbeiträge präsentiert, ferner 8 Praxisbeiträge, 2 Positionspapiere, 9 Demo-Sessions und 16 Poster. Die angenommenen Beiträge finden sich im vorliegenden Tagungsband, zusammen mit drei kurzen Statements der eingeladenen Keynote-Sprecher. Zusammen mit dem Programmkomitee konnten wir so ein qualitativ hochwertiges, interessantes und abwechslungsreiches Programm zusammenstellen. Allen Autorinnen und Autoren sowie unserem Programmkomitee gilt unser herzlicher Dank. Die 20. Fachtagung Bildungstechnologien ist eine Jubiläumstagung. Zwei Jahrzehnte der Kontinuität, aber auch der stetigen Weiterentwicklung begründen in unserem Fachgebiet schon eine besondere Tradition. Auch wenn mancherorts die Corona-Pandemie noch das Denken beherrscht und für Einschränkungen im Konferenzbetrieb und bei Dienstreisen sorgt, haben wir deshalb den Schritt zurück in die Normalität getan und die Konferenz als Präsenzveranstaltung konzipiert. Wir freuen uns darüber, Sie auf der 20. Fachtagung Bildungstechnologien in Karlsruhe zu begrüßen!},
	language = {de},
	number = {P-322},
	urldate = {2022-09-11},
	institution = {Köllen},
	editor = {{Gesellschaft für Informatik e.V. (GI)}},
	collaborator = {Henning, Peter A. and Striewe, Michael and Wölfel, Matthias},
	month = sep,
	year = {2022},
	note = {OCLC: 1343886037},
	keywords = {Lehr- und Lerneffektivität, Technologieintegration, Promotion:FU2a, Promotion:Relevanz:5, Promotion:Kerngedanke, Promotion:Literaturanalyse, Charité:Promotion, Promotion:Literaturanalyse:Berichte},
	file = {Henning et al. - 2022 - DELFI 2022 20. Fachtagung Bildungstechnologien de.pdf:/Users/jochenhanisch-johannsen/Zotero/storage/N7YF7BG8/Henning et al. - 2022 - DELFI 2022 20. Fachtagung Bildungstechnologien de.pdf:application/pdf},
}

@techreport{hilz_e-learning_nodate,
	title = {E-{Learning} {Unterrichtskonzepte} für die {Fahranfängervorbereitung}},
	language = {de},
	author = {Hilz, Jana},
	keywords = {Promotion:Literaturanalyse, Charité:Promotion, Promotion:Literaturanalyse:Berichte},
	pages = {69},
	file = {Hilz - E-Learning Unterrichtskonzepte für die Fahranfänge.pdf:/Users/jochenhanisch-johannsen/Zotero/storage/QMB6PLKH/Hilz - E-Learning Unterrichtskonzepte für die Fahranfänge.pdf:application/pdf},
}

@article{raitner_agile_2021,
	title = {Die agile {Transformation}: {Groß} denken, klein beginnen, schnell lernen},
	volume = {5},
	issn = {2569-1996},
	shorttitle = {Die agile {Transformation}},
	url = {http://link.springer.com/10.1007/s42354-021-0348-2},
	doi = {10/gq5pk7},
	language = {de},
	number = {2},
	urldate = {2022-10-30},
	journal = {Digitale Welt},
	author = {Raitner, Marcus},
	month = apr,
	year = {2021},
	keywords = {Promotion:Ausschluss, Promotion:Literaturanalyse, Charité:Promotion},
	pages = {110--111},
	file = {Raitner - 2021 - Die agile Transformation Groß denken, klein begin.pdf:/Users/jochenhanisch-johannsen/Zotero/storage/4REET9RK/Raitner - 2021 - Die agile Transformation Groß denken, klein begin.pdf:application/pdf},
}

@misc{noauthor_agile_2021,
	title = {Das {Agile} {Manifest} - nicht nur für {Software}-{Firmen}},
	url = {https://synapsenstau.de/agiles-manifest/},
	abstract = {Es ist eine Antwort auf die immer komplexer werdende Umwelt. Wie können wir überhaupt noch klar kommen? Indem wir uns anpassen und schnelle, flexible Methoden anwenden.},
	language = {de-DE},
	urldate = {2022-11-17},
	journal = {Synapsenstau.de},
	month = feb,
	year = {2021},
	keywords = {Bildung, Multimedia, FernUni-Hagen:MABM:Master-Arbeit, Promotion:Literaturanalyse, Charité:Promotion},
	file = {Snapshot:/Users/jochenhanisch-johannsen/Zotero/storage/TMNWCDGZ/agiles-manifest.html:text/html},
}

@book{graf_agiles_2019,
	address = {Freiburg München Stuttgart},
	edition = {2. Aufl.},
	title = {Agiles {Lernen}: {Neue} {Rollen}, {Kompetenzen} und {Methoden} im {Unternehmenskontext}},
	isbn = {978-3-648-13060-5},
	shorttitle = {Agiles {Lernen}},
	language = {de-DE},
	publisher = {Haufe Group},
	author = {Graf, Nele and Gramß, Denise and Edelkraut, Frank},
	year = {2019},
	keywords = {Bildung, Multimedia, FernUni-Hagen:MABM:Master-Arbeit, Promotion:Literaturanalyse, Charité:Promotion},
}

@techreport{rathgeb_jugend_2022,
	address = {Stuttgart},
	type = {Forschungsbericht},
	title = {Jugend, {Information}, {Medien}: {Basisuntersuchung} zum {Medienumgang} 12- bis 19-{Jähriger}},
	shorttitle = {Jugend, {Information}, {Medien}},
	url = {https://www.mpfs.de/fileadmin/files/Studien/JIM/2022/JIM_2022_Web_final.pdf},
	language = {de-DE},
	urldate = {2023-01-26},
	institution = {Medienpädagogischer Forschungsverbund Südwest (mpfs)},
	author = {Rathgeb, Thomas and Schmid, Thomas},
	editor = {{mpfs}},
	year = {2022},
	keywords = {Bildung, Multimedia, Promotion:Literaturanalyse, Charité:Promotion, Promotion:Literaturanalyse:Berichte},
	pages = {68},
	file = {JIM_2022_Charts.pdf:/Users/jochenhanisch-johannsen/Zotero/storage/I3CUXT5Y/JIM_2022_Charts.pdf:application/pdf;Rathgeb und Schmid - 2022 - Jugend, Information, Medien Basisuntersuchung zum.pdf:/Users/jochenhanisch-johannsen/Zotero/storage/3WJ6NIMX/Rathgeb und Schmid - 2022 - Jugend, Information, Medien Basisuntersuchung zum.pdf:application/pdf},
}

@phdthesis{clas_moglichkeiten_nodate,
	type = {Bachelorarbeit},
	title = {Möglichkeiten und {Grenzen} des {Einsatzes} von {Blended} {Learning} im {Medizinstudium}},
	language = {de},
	author = {Claß, Simone},
	keywords = {Bildung, Multimedia, Promotion:Literaturanalyse, Charité:Promotion},
	file = {Claß - Möglichkeiten und Grenzen des Einsatzes von Blende.pdf:/Users/jochenhanisch-johannsen/Zotero/storage/6VNWUA3I/Claß - Möglichkeiten und Grenzen des Einsatzes von Blende.pdf:application/pdf},
}

@techreport{engelien_geneme_1998,
	address = {Dresden},
	type = {Konferenzbericht},
	title = {{GeNeMe} 98. {Gemeinschaften} in {Neuen} {Medien}},
	copyright = {Deutsches Urheberrecht},
	shorttitle = {{GeNeMe98}},
	url = {https://www.pedocs.de/frontdoor.php?source_opus=22387},
	abstract = {Das Konzept der Virtual Community verkörpert mehr als nur eine Renaissance des "Virtuellen Unternehmens". Aufgrund der Verfügbarkeit globaler Netze und leistungsfähiger Software-Technologie verspricht die Neugestaltung betrieblicher Prozesse und Organisationsstrukturen jetzt Wirklichkeit zu werden. Virtuelle Organisationsformen werden durch moderne Informationstechnologien unterstützt und teilweise erst ermöglicht, basieren in ihrem Wesen jedoch auf einem sich weltweit vollziehenden Wechsel im Wirtschaftsparadigma hin zu agiler Produktion. Dies stellt an Wissenschaftler und Praktiker gleichermaßen hohe Anforderungen bezüglich der Konzipierung, der Entwicklung und des Betriebes von Informationssystemen. Muster der Gestaltung von technischen Infrastrukturen müssen an die neuen Formen der Unternehmenskooperationen ebenso angepasst werden wie Methoden und Muster für die Analyse und den Entwurf von Anwendungssystemen. Schwerpunkt dieser Publikation ist die semantische Modellierung der Anwendungs- und Systemarchitektur. Die Autoren möchten damit zum Dialog zwischen Hochschul- und Unternehmensvertretern, schwerpunktmäßig von mittelständischen Unternehmen, beitragen. Die hier vorgestellten Erkenntnisse entstammen mehreren Studien und Projekten, die in einer Reihe von Hochschulen und Unternehmen durchgeführt wurden und werden. (DIPF/Orig.)},
	language = {de-DE},
	number = {Band 2},
	urldate = {2023-02-17},
	institution = {Universität Dresden, Fakultät Informatik, Institut für Informa­ tionssysteme, Dozentur „Entwurfmethoden und Werkzeuge für Anwendugssysteme“},
	editor = {{Norbert Szyperski,} and {Udo Winand} and {Dietrich Seibt} and {Rainer Kuhlen}},
	collaborator = {Engelien, Martin and Bender, Kai},
	year = {1998},
	note = {Publisher: Josef Eul Verlag},
	keywords = {Deutschland, Germany, Virtual learning, Digitalisierung, Digitalization, Virtuelle Arbeitswelt, Technological development, Technologische Entwicklung, Bildung, Hochschullehre, University lecturing, University teaching, Digitale Bildung, Internet, Computer science, Informatics, Informatik, Anwendung, Multimedia, Deployment of media, Medieneinsatz, Use of media, Information technology, Informationsmanagement, Informationssystem, Informationstechnologie, Infrastructure, Infrastruktur, Konferenzschrift, Network, Netzwerk, Neue Medien, New media, Softwaretechnologie, Telearbeit, Telework, Unternehmen, Unternehmensorganisation, Virtualisierung, Virtuelle Gemeinschaft, Virtuelles Unternehmen, Wirtschaftspädagogik, World Wide Web, Technologieintegration, Systemanpassung, Krisenreaktion im Bildungsbereich, Promotion:FU4b, Promotion:Relevanz:2, Promotion:Weiterführung, Promotion:Literaturanalyse, Charité:Promotion, Promotion:Literaturanalyse:Berichte},
	pages = {347},
	file = {Engelien und Bender - 1998 - GeNeMe 98. Gemeinschaften in Neuen Medien. TU Dres.pdf:/Users/jochenhanisch-johannsen/Zotero/storage/Q7PX6XA6/Engelien und Bender - 1998 - GeNeMe 98. Gemeinschaften in Neuen Medien. TU Dres.pdf:application/pdf},
}

@techreport{klaus_virtuelle_2021,
	address = {Dresden},
	title = {Virtuelle {Organisation} und {Neue} {Medien} 2006. {Workshop} {GeNeMe} 2006, {Gemeinschaften} in {Neuen} {Medien}. {TU} {Dresden}, 28./29.09.2006},
	copyright = {Deutsches Urheberrecht},
	url = {https://www.pedocs.de/frontdoor.php?source_opus=22394},
	abstract = {Nunmehr zum neunten Male findet die Tagungsreihe „GeNeMe - Gemeinschaften in Neuen Medien“ mit einer Vielzahl interessanter Beiträge in folgenden Rubriken statt: Konzepte für GeNeMe (Geschäfts-, Betriebs- und Architektur-Modelle); IT-Stützung (Portale, Plattformen, Engines) von GeNeMe; Soziologische, psychologische, personalwirtschaftliche, didaktische und rechtliche Aspekte von GeNeMe; E-Learning in GeNeMe; Wissensmanagement in GeNeMe und Anwendungen und Praxisbeispiele von GeNeMe. (DIPF/Orig.)},
	language = {de},
	urldate = {2023-02-17},
	institution = {Technische Universität Dresden – Fakultät Informatik Professur für Multimediatechnik, Privat-Dozentur für Angewandte Informatik,},
	collaborator = {Klaus, Meißner and Martin, Engelien},
	month = jun,
	year = {2021},
	note = {Publisher: TUDpress},
	keywords = {Deutschland, Germany, Learning, Ethik, Hochschule, Virtual learning, Digitalisierung, Digitalization, Arbeitsprozess, Educational Environment, Learning environment, Working process, On line, Online, Virtuelle Arbeitswelt, Technological development, Technologische Entwicklung, Bildung, Technologie, Technology, Hochschullehre, University lecturing, University teaching, Digitale Bildung, Cooperation, Kooperation, Practice, Praxis, Fundamental concepts, Konzept, Internet, Virtuelle Lehre, Computerunterstütztes Verfahren, Anwendung, Multimedia, Lernplattform, Information technology, Informationsmanagement, Informationssystem, Informationstechnologie, Konferenzschrift, Network, Netzwerk, Neue Medien, New media, Unternehmensorganisation, Virtuelle Gemeinschaft, Virtuelles Unternehmen, World Wide Web, Company organization, Geschäftsmodell, Information system, Knowledge management, Knowledge society, Kommunikationstechnik, Lerngemeinschaft, Mobiles Gerät, Software, Unternehmenskommunikation, Web based instruction, Web Based Training, Wissensgesellschaft, Wissensmanagement, Elektronische Kommunikation, Virtuelle Organisation, Sozialwissenschaft, Technologieintegration, Systemanpassung, Krisenreaktion im Bildungsbereich, Promotion:FU4b, Promotion:Relevanz:4, Promotion:Weiterführung, Promotion:Literaturanalyse, Charité:Promotion, Promotion:Literaturanalyse:Berichte},
	file = {Klaus und Martin - 2021 - Virtuelle Organisation und Neue Medien 2006. Works.pdf:/Users/jochenhanisch-johannsen/Zotero/storage/Y8QUMGEQ/Klaus und Martin - 2021 - Virtuelle Organisation und Neue Medien 2006. Works.pdf:application/pdf},
}

@techreport{robra-bissantz_teaching_2019,
	address = {Braunschweig},
	type = {Konferenzbericht},
	title = {Teaching {Trends} 2018. {Die} {Präsenzhochschule} und die digitale {Transformation}},
	copyright = {Deutsches Urheberrecht},
	shorttitle = {Teaching {Trends} 2018},
	url = {https://www.pedocs.de/frontdoor.php?source_opus=17455},
	abstract = {Der Band bietet spannende Einblicke in Präsenzhochschulen, die in geschickten Szenarien verschiedene digitale Medien für den Kompetenzerwerb ihrer Studierenden nutzen. In einer breiten Sicht auf die Digitalisierung beschäftigen sich die Tagungsbeiträge mit neuen Lernformaten wie Blended Learning und Inverted Classroom, deren aktuellen rechtlichen Rahmenbedingungen in DSGVO und Urheberrecht und technischen Grundlagen, z.B. in Augmented / Virtual Reality oder Audience Response. Darüber hinaus jedoch kommen übergreifende Strategien und Entwicklungskonzepte zu Wort, die die Hochschule in eine digitale Zukunft führen. In allen Bereichen berichteten die Vortragenden sowohl direkt aus ihrer Lehrpraxis als auch aus der begleitenden Forschung. Zur Abrundung der Tagung haben die Herausgeber*innen das einleitende Streitgespräch zur Bedeutung der digitalen Transformation für Universitäten, die Podiumsdiskussion zu Herausforderungen, die sich daraus für das Studium ergeben, sowie eine Keynote zur Architektur von Lernräumen zu Papier gebracht. (DIPF/Orig.)},
	language = {de-DE},
	number = {Band 7},
	urldate = {2023-02-17},
	institution = {Waxmann Verlag GmbH},
	editor = {{ELAN e.V.}},
	collaborator = {Robra-Bissantz, Susanne and Bott, Oliver J. and Kleinefeld, Norbert and Neu, Kevin and Zickwolf, Katharina},
	year = {2019},
	note = {Publisher: Waxmann},
	keywords = {Deutschland, Germany, Kompetenz, Hochschule, University didactics, Virtual learning, Digitalisierung, Digitalization, Methode, Bildung, Hochschullehre, University lecturing, University teaching, Digitale Bildung, Virtuelle Hochschule, Hochschulentwicklung, University development, Multimedia, Learning method, Learning techniques, Präsenzhochschule, Deployment of media, Medieneinsatz, Use of media, Konferenzschrift, Virtualisierung, Individualized education programs, Promotion:todo, Promotion:Literaturanalyse, Charité:Promotion, Promotion:Literaturanalyse:Berichte},
	pages = {232},
	file = {Robra-Bissantz et al. - 2019 - Teaching Trends 2018. Die Präsenzhochschule und di.pdf:/Users/jochenhanisch-johannsen/Zotero/storage/F87GRZR3/Robra-Bissantz et al. - 2019 - Teaching Trends 2018. Die Präsenzhochschule und di.pdf:application/pdf},
}

@techreport{elan_ev_teaching_2014,
	address = {Oldenburg},
	type = {Konferenzbericht},
	title = {Teaching {Trends} 2014. {Offen} für neue {Wege}: {Digitale} {Medien} in der {Hochschule}},
	copyright = {Deutsches Urheberrecht},
	shorttitle = {Teaching {Trends} 2014},
	url = {https://www.pedocs.de/frontdoor.php?source_opus=18458},
	abstract = {Das Lernen und Lehren mit digitalen Medien an Hochschulen hat stark an Bedeutung gewonnen und wird durch aktuelle Programme des BMBF wie den "Qualitätspakt Lehre" oder "Aufstieg durch Bildung - offene Hochschulen" weiter gefördert. Digitale Medien können einen wichtigen Beitrag für hochschuldidaktische Innovation, für mehr Durchlässigkeit und die weitere Öffnung der Hochschulen für neue Zielgruppen leisten. In diesem Sammelband zum ELAN e.V.-Kongress "TEACHINGTRENDS14: Offen für neue Wege: Digitale Medien in der Hochschule" werden empirische Ergebnisse, Beispiele und Erfahrungsberichte zur Umsetzung und Integration didaktischer und technologischer Trends in der Hochschullehre in den Blick genommen. Schwerpunkte bilden hierbei digitale Medien für das forschende Lernen, heterogene Zielgruppen sowie neue Bildungstechnologien und Medienkompetenz. (Verlag)},
	language = {de-DE},
	number = {Band 2},
	urldate = {2023-02-17},
	institution = {Waxmann Verlag GmbH},
	editor = {{ELAN e.V.} and {ELAN}},
	collaborator = {Zawacki-Richter, Olaf and Kergel, David and Kleinefeld, Norbert and Muckel, Petra and Stöter, Joachim and Brinkmann, Katrin},
	year = {2014},
	note = {Publisher: Waxmann},
	keywords = {Deutschland, Germany, Informations- und Kommunikationstechnologie, Kompetenz, Hochschule, Studierender, University didactics, Media competence, Media skills, Educational Environment, Learning environment, Lernumgebung, Bildung, Hochschullehre, University lecturing, University teaching, Digitale Bildung, Hochschulbildung, Computer-assisted instruction, Academic studies, Studium, Target group, Zielgruppe, Multimedia, Agilität, Heterogeneity, Heterogenität, Computer based training, Computer-aided instruction, Computerunterstützter Unterricht, Lower Saxony, Niedersachsen, Programmed instruction, Digitale Medien:Bildungsinformatik, Promotion:todo, FernUni-Hagen:MABM:Master-Arbeit, Promotion:Literaturanalyse, Charité:Promotion, Promotion:Literaturanalyse:Berichte},
	pages = {267},
	file = {Zawacki-Richter et al. - 2014 - Teaching Trends 2014. Offen für neue Wege Digital.pdf:/Users/jochenhanisch-johannsen/Zotero/storage/3IGR7LJ8/Zawacki-Richter et al. - 2014 - Teaching Trends 2014. Offen für neue Wege Digital.pdf:application/pdf},
}

@techreport{elan_ev_teaching_2016,
	address = {Clausthal},
	type = {Konferenzbericht},
	title = {Teaching {Trends} 2016. {Digitalisierung} in der {Hochschule}: {Mehr} {Vielfalt} in der {Lehre}},
	copyright = {Deutsches Urheberrecht},
	shorttitle = {Teaching {Trends} 2016},
	url = {https://www.pedocs.de/frontdoor.php?source_opus=15136},
	abstract = {Die Digitalisierung an Hochschulen gewinnt immer mehr an Bedeutung. Digitalisierung kann einen wichtigen Beitrag für hochschuldidaktische Innovationen, für mehr Durchlässigkeit und die weitere Öffnung der Hochschulen für neue Zielgruppen leisten und so mehr Vielfalt in der Lehre generieren. In diesem Sammelband zum ELAN e.V. Kongress „TEACHINGTRENDS16: Digitalisierung in der Hochschule: Mehr Vielfalt in der Lehre“ werden empirische Ergebnisse, Beispiele und Erfahrungsberichte zur Umsetzung und Integration didaktischer und technologischer Trends in der Hochschullehre in den Blick genommen. Schwerpunkte bilden hierbei die Diversität in der Lehre, individualisiertes Lehren und Lernen mit digitalen Medien sowie die Erfolgsfaktoren des Einsatzes digitaler Medien an Hochschulen. (DIPF/Orig.)},
	language = {de-DE},
	number = {Band 5},
	urldate = {2023-02-17},
	institution = {Waxmann Verlag GmbH},
	editor = {{ELAN e.V.}},
	collaborator = {Pfau, Wolfgang and Baetge, Caroline and Bedenlier, Svenja Mareike and Kramer, Carina and Stöter, Joachim},
	year = {2016},
	note = {Publisher: Waxmann},
	keywords = {University didactics, Digitalisierung, Digitalization, Bildung, Hochschullehre, University lecturing, University teaching, Digitale Bildung, Deployment of media, Medieneinsatz, Use of media, Konferenzschrift, Individualized education programs, Promotion:Literaturanalyse, Charité:Promotion, Promotion:Literaturanalyse:Berichte},
	pages = {249},
	file = {Pfau et al. - 2016 - Teaching Trends 2016. Digitalisierung in der Hochs.pdf:/Users/jochenhanisch-johannsen/Zotero/storage/6F3XEBF8/Pfau et al. - 2016 - Teaching Trends 2016. Digitalisierung in der Hochs.pdf:application/pdf},
}

@misc{noauthor_learning_nodate,
	title = {Learning {Analytics} – {Didaktischer} {Benefit} zur {Verbesserung} von {Lehr}-{Lernprozessen}? {Implikationen} aus dem {Einsatz} von {Learning} {Analytics} im {Hochschulkontext} - bwp@ {Berufs}- und {Wirtschaftspädagogik} - online},
	shorttitle = {Learning {Analytics} – {Didaktischer} {Benefit} zur {Verbesserung} von {Lehr}-{Lernprozessen}?},
	url = {https://www.bwpat.de/ausgabe/40/lipp-etal},
	abstract = {1\&nbsp;\&nbsp;\&nbsp;\&nbsp;\&nbsp;\&nbsp;\&nbsp;\&nbsp;\&nbsp; EinleitungDas Voranschreiten der digitalen Transformation stößt gesamtgesellschaftliche Entwicklungen an, welche sich auch auf das Bildungssystem erstrecken (vgl. Gloerfeld 2020, 4).},
	language = {de-de},
	urldate = {2023-02-20},
	keywords = {Bildung, Multimedia, Promotion:Literaturanalyse, Charité:Promotion, Leraning:Analytics},
	file = {Snapshot:/Users/jochenhanisch-johannsen/Zotero/storage/XAZWDR56/lipp-etal.html:text/html},
}

@phdthesis{josu_implementierung_nodate,
	title = {{IMPLEMENTIERUNG} {MODERNER} {DIGITALER} {MEDIEN} {UND} {WERKZEUGE} {IM} {DaF}-{UNTERRICHT}},
	language = {de},
	author = {Josu, Natalia and Kononova, Tatiana},
	keywords = {Bildung, Multimedia, Promotion:Literaturanalyse, Charité:Promotion},
	file = {Josu und Kononova - IMPLEMENTIERUNG MODERNER DIGITALER MEDIEN UND WERK.pdf:/Users/jochenhanisch-johannsen/Zotero/storage/HV9RQQBD/Josu und Kononova - IMPLEMENTIERUNG MODERNER DIGITALER MEDIEN UND WERK.pdf:application/pdf},
}

@incollection{ifenthaler_gelingensbedingungen_nodate,
	title = {Gelingensbedingungen für produktive {Learning}-{Analytics}-­ {Systeme}},
	language = {de},
	author = {Ifenthaler, Dirk},
	keywords = {⛔ No DOI found, Promotion:Literaturanalyse, Charité:Promotion, Leraning:Analytics},
	file = {Ifenthaler - Gelingensbedingungen für produktive Learning-Analy.pdf:/Users/jochenhanisch-johannsen/Zotero/storage/J7D5B5AL/Ifenthaler - Gelingensbedingungen für produktive Learning-Analy.pdf:application/pdf},
}

@misc{martinez-maldonado_lessons_2023,
	title = {Lessons {Learnt} from a {Multimodal} {Learning} {Analytics} {Deployment} {In}-the-wild},
	url = {http://arxiv.org/abs/2303.09099},
	abstract = {CCS Concepts: • Human-centered computing → Ubiquitous and mobile computing; • Applied computing → Collaborative learning; • Computer systems organization → Embedded systems; Redundancy; Robotics; • Networks → Network reliability.},
	language = {en},
	urldate = {2023-03-24},
	publisher = {arXiv},
	author = {Martinez-Maldonado, Roberto and Echeverria, Vanessa and Fernandez-Nieto, Gloria and Yan, Lixiang and Zhao, Linxuan and Alfredo, Riordan and Li, Xinyu and Dix, Samantha and Jaggard, Hollie and Wotherspoon, Rosie and Osborne, Abra and Gašević, Dragan and Shum, Simon Buckingham},
	month = mar,
	year = {2023},
	note = {arXiv:2303.09099 [cs]},
	keywords = {Computer Science - Human-Computer Interaction, Promotion:Literaturanalyse, Charité:Promotion, Leraning:Analytics},
	file = {Martinez-Maldonado et al. - 2023 - Lessons Learnt from a Multimodal Learning Analytic.pdf:/Users/jochenhanisch-johannsen/Zotero/storage/PUB9VHNJ/Martinez-Maldonado et al. - 2023 - Lessons Learnt from a Multimodal Learning Analytic.pdf:application/pdf},
}

@phdthesis{bohmer_auswahl_nodate,
	title = {Auswahl und {Erprobung} einer {Quiz}-{App} als zukunftsträchtige {E}-{Learning}-{Komponente}},
	language = {de},
	author = {Böhmer, Henrike},
	keywords = {Bildung, Multimedia, Promotion:Literaturanalyse, Charité:Promotion},
	file = {Böhmer - Auswahl und Erprobung einer Quiz-App als zukunftst.pdf:/Users/jochenhanisch-johannsen/Zotero/storage/S2JQEWD4/Böhmer - Auswahl und Erprobung einer Quiz-App als zukunftst.pdf:application/pdf},
}

@phdthesis{keller_sexting_2023,
	address = {Luzern},
	type = {Bachelor-{Arbeit}},
	title = {Sexting bei {Jugendlichen}: {Welche} {Verantwortungen} ergeben sich für {Professionelle} der {Sozialen} {Arbeit} im {Umgang} mit {Phänomenen} im digitalen {Raum} wie {Sexting}?},
	shorttitle = {Sexting bei {Jugendlichen}},
	url = {https://files.www.soziothek.ch/source/2023_ba_Keller%20Leslie.pdf},
	abstract = {Durch den digitalen Wandel und die damit verbundenen technologischen Fortschritte hat sich das mediale Nutzungsverhalten von Jugendlichen verändert und die Entstehung von neuen sexualbezogenen Phänomenen im virtuellen Raum ist begünstigt worden. Sexting ist eines dieser Phänomene und bezeichnet den privaten und freiwilligen Austausch von erotischem Bild- und Videomaterial. Medial in Erscheinung trat der Begriff vor zirka 15 Jahren, als missbräuchlich weitergeleitete und veröffentlichte Sexts schwerwiegende Folgen für junge Menschen hatten. Dies führte zu einem wissenschaftlichen und öffentlichen Diskurs, der die Risiken von Sexting in den Vordergrund stellte und es als abweichendes Verhalten klassifizierte. Mit der vorliegenden Arbeit wird aufgezeigt, dass Sexting in der heutigen Lebenswelt von Jugendlichen zentral ist und es sich dabei nicht um eine Abnormalität, sondern um eine Strategie handelt, um ganz unterschiedliche motivierte Bedürfnisse zu befriedigen. Die Risiken, die sich durch die Praktizierung von Sexting ergeben können, werden dabei nicht ausgeblendet, jedoch wird eine lebensweltorientierte Herangehensweise an die Thematik gewählt, in der es die Jugendlichen in einer selbstbestimmten Sexualität zu befähigen gilt. Dafür werden wichtige Aspekte aufgezeigt, die für ein professionelles Handeln der Sozialen Arbeit berücksichtigt werden müssen, wie die Aufklärung im digitalen Zeitalter, erforderliche (Medien-)Kompetenzen und sinnvolle Präventionsansätze. Am Schluss wird auf die Verantwortlichkeiten der Sozialen Arbeit eingegangen, die sich im Umgang mit Jugendlichen, die Sexting betreiben, ergeben und so wird die zentrale Fragestellung der vorliegenden Literaturarbeit beantwortet.},
	language = {de-CH},
	urldate = {2023-03-25},
	school = {Hochschule Luzern, Soziale Arbeit},
	author = {Keller, Leslie},
	month = jan,
	year = {2023},
	keywords = {Sexualität, Promotion:Literaturanalyse, Charité:Promotion},
	file = {Keller - 2023 - Sexting bei Jugendlichen Welche Verantwortungen e.pdf:/Users/jochenhanisch-johannsen/Zotero/storage/Z2VNJT4D/Keller - 2023 - Sexting bei Jugendlichen Welche Verantwortungen e.pdf:application/pdf},
}

@phdthesis{altschaffl_achtsame_2022,
	title = {Achtsame {Erreichbarkeit}},
	language = {de},
	author = {Altschäffl, Pia},
	month = aug,
	year = {2022},
	keywords = {Promotion:Literaturanalyse, Charité:Promotion},
	file = {Altschäffl - 2022 - Achtsame Erreichbarkeit.pdf:/Users/jochenhanisch-johannsen/Zotero/storage/VY9BBYC9/Altschäffl - 2022 - Achtsame Erreichbarkeit.pdf:application/pdf},
}

@techreport{bernadette_dilger_seamless_nodate,
	type = {Werkstattbericht},
	title = {Seamless {Learning} as an approach to foster flexible learning in higher {educationSeamless} {Learning} als {Ansatz} zum {Umgang} mit flexiblem {Lehren} und {Lernen} – {Erfahrungs}-bericht aus dem {Seamless} {Learning} {Lab}},
	url = {https://www.zfhe.at/index.php/zfhe/article/view/1248},
	abstract = {Seamless learning focuses on a challenge characteristic of flexible learning, whereby learning occurs in different contexts. Learning across contexts affords certain opportunities (e.g., connecting abstract principles learned within a formal context with life experience), but also comes with a certain level of risk (e.g., fragmentation of learning experiences). Within an ongoing EU-funded research project, seven seamless learning conceptions are being developed, implemented and investigated using a design-based research approach. However, this process necessitates a considerable amount of consultancy. To ensure long-term sustainability and scalability, an open and accessible consultancy concept was developed that included ICT support. This paper briefly introduces the project and places it within its theoretical context, as well as discussing the experiences and findings gained during the development of the lighthouses (which serve as the basis of the consultancy concept) and the tool developed.},
	language = {de},
	urldate = {2023-03-31},
	author = {Bernadette Dilger and Luci Gommers and Christian Rapp and Marco Trippel and Andreas Butz and Simon Huff and Rainer Mueller and Ralf Schimkat},
	note = {Publisher: Verein Forum neue Medien in der Lehre Austria},
	keywords = {Bildung, Digitale Bildung, Seamless Learning, Promotion:Literaturanalyse, Charité:Promotion, Promotion:Literaturanalyse:Berichte},
	file = {Bernadette Dilger et al. - Seamless Learning as an approach to foster flexibl.pdf:/Users/jochenhanisch-johannsen/Zotero/storage/HGHKWWKR/Bernadette Dilger et al. - Seamless Learning as an approach to foster flexibl.pdf:application/pdf},
}

@book{chejara_multimodal_2023,
	title = {Multimodal {Learning} {Analytics} research in the wild: challenges and their potential solutions},
	shorttitle = {Multimodal {Learning} {Analytics} research in the wild},
	abstract = {Multimodal Learning Analytics (MMLA) has enabled researchers to address learning in physical settings which have long been either overlooked or studied using observational methods. With the use of sensors, researchers have been able to understand learning through an entirely new perspective (e.g., analyzing heart-rate variability to find collaboration indicators). Consequently, MMLA has grown significantly in the past few years, moving from a nascent stage towards a more mature field. It raises a question on how the MMLA researcher can move further, i.e., the transition towards practice which started getting researchers' attention. This paper discusses the challenges we faced while conducting MMLA studies in classroom settings over four years and potential solutions to realize the goal of transitioning MMLA research to educational practice. This paper aims to start a discussion in the field of MMLA over the transition of research to practice.},
	author = {Chejara, Pankaj and Kasepalu, Reet and Prieto, Luis and Rodríguez-Triana, María and Ruiz-Calleja, Adolfo and Shankar, Shashi Kant},
	month = mar,
	year = {2023},
	keywords = {Promotion:Literaturanalyse, Charité:Promotion, Leraning:Analytics},
}

@incollection{felgentreu_gelingensbedingungen_2023,
	address = {Wiesbaden},
	title = {Gelingensbedingungen für produktive {Learning}-{Analytics}-{Systeme}},
	isbn = {978-3-658-38543-9 978-3-658-38544-6},
	url = {https://link.springer.com/10.1007/978-3-658-38544-6_13},
	language = {de},
	urldate = {2023-04-07},
	booktitle = {Bildung und {Medien}},
	publisher = {Springer Fachmedien Wiesbaden},
	author = {Ifenthaler, Dirk},
	editor = {Felgentreu, Jessica and Gloerfeld, Christina and Grüner, Claudia and Karolyi, Heike and Leineweber, Christian and Weßler, Linda and Wrede, Silke E.},
	year = {2023},
	doi = {10.1007/978-3-658-38544-6_13},
	keywords = {Bildung, Multimedia, Promotion:Literaturanalyse, Charité:Promotion, Leraning:Analytics},
	pages = {205--221},
	file = {Ifenthaler - 2023 - Gelingensbedingungen für produktive Learning-Analy.pdf:/Users/jochenhanisch-johannsen/Zotero/storage/F6NY6Z44/Ifenthaler - 2023 - Gelingensbedingungen für produktive Learning-Analy.pdf:application/pdf},
}

@techreport{korge_lernen_2022,
	address = {Stuttgart},
	title = {Lernen zwischen {Tradition} und {Transformation}: {Eine} {Erhebung} zu digitalem {Lernen} von {Betriebs}- und {Personalratsmitgliedern}},
	url = {https://publica.fraunhofer.de/handle/publica/414536},
	abstract = {Der Studienreport basiert auf einer Befragung von über 400 Betriebs- und Personalratsmitgliedern zu deren Bedarfen, Vorlieben und Voraussetzungen für digital unterstütztes Lernen. Die ausführliche Ergebnisdarstellung mit zahlreichen Grafiken sowie umfassende Schlussfolgerungen geben Anhaltspunkte zur Entwicklung von zielgruppengerechten Weiterbildungsangeboten. Die Studie ist Teil des BMBF-geförderten Verbundprojektes DigiLab NPO (FKZ 02L18A230 ff.).},
	language = {de-DE},
	urldate = {2023-04-14},
	institution = {Fraunhofer-Institut für Arbeitswirtschaft und Organisation},
	author = {Korge, Gabriele and Wolter, Maxie and Hamann, Karin and Zaiser, Helmut},
	editor = {Nägele, Rainer and Wilde, Ralf},
	collaborator = {Fraunhofer-Gesellschaft},
	year = {2022},
	note = {Publisher: Fraunhofer Verlag},
	keywords = {Weiterbildung, Bildung, Digitale Bildung, Gesetzliche Interessenvertretung, Lernbedarf, Lernvorlieben, Umfrageergebnisse, Promotion:Literaturanalyse, Charité:Promotion, Promotion:Literaturanalyse:Berichte},
	pages = {67},
	file = {Korge et al. - 2022 - Lernen zwischen Tradition und Transformation Eine.pdf:/Users/jochenhanisch-johannsen/Zotero/storage/KZ6NDTKU/Korge et al. - 2022 - Lernen zwischen Tradition und Transformation Eine.pdf:application/pdf},
}

@phdthesis{noauthor_masterarbeit_nodate,
	title = {Masterarbeit 2019\_Kittel {Laura}.pdf},
	url = {https://irf.fhnw.ch/bitstream/handle/11654/27950/Masterarbeit%202019_Kittel%20Laura.pdf?sequence=1&isAllowed=y},
	urldate = {2023-04-14},
	keywords = {Feedback, Promotion:Literaturanalyse, Charité:Promotion},
	file = {Masterarbeit 2019_Kittel Laura.pdf:/Users/jochenhanisch-johannsen/Zotero/storage/ASHNM6PN/Masterarbeit 2019_Kittel Laura.pdf:application/pdf},
}

@phdthesis{noauthor_diss-moench2011pdf_nodate,
	title = {diss-moench2011.pdf},
	url = {https://dl.gi.de/bitstream/handle/20.500.12116/35789/diss-moench2011.pdf?sequence=1&isAllowed=y},
	urldate = {2023-04-14},
	keywords = {Promotion:Literaturanalyse, Charité:Promotion},
	file = {diss-moench2011.pdf:/Users/jochenhanisch-johannsen/Zotero/storage/2L5L9TR7/diss-moench2011.pdf:application/pdf},
}

@misc{becerra_m2lads_2023,
	title = {{M2LADS}: {A} {System} for {Generating} {MultiModal} {Learning} {Analytics} {Dashboards} in {Open} {Education}},
	shorttitle = {{M2LADS}},
	url = {http://arxiv.org/abs/2305.12561},
	abstract = {In this article, we present a Web-based System called M2LADS, which supports the integration and visualization of multimodal data recorded in learning sessions in a MOOC in the form of Web-based Dashboards. Based on the edBB platform, the multimodal data gathered contains biometric and behavioral signals including electroencephalogram data to measure learners' cognitive attention, heart rate for affective measures, visual attention from the video recordings. Additionally, learners' static background data and their learning performance measures are tracked using LOGCE and MOOC tracking logs respectively, and both are included in the Web-based System. M2LADS provides opportunities to capture learners' holistic experience during their interactions with the MOOC, which can in turn be used to improve their learning outcomes through feedback visualizations and interventions, as well as to enhance learning analytics models and improve the open content of the MOOC.},
	urldate = {2023-05-26},
	publisher = {arXiv},
	author = {Becerra, Álvaro and Daza, Roberto and Cobos, Ruth and Morales, Aythami and Cukurova, Mutlu and Fierrez, Julian},
	month = may,
	year = {2023},
	note = {arXiv:2305.12561 [cs]},
	keywords = {Computer Science - Human-Computer Interaction, Computer Science - Computer Vision and Pattern Recognition, Promotion:Literaturanalyse, Charité:Promotion, Leraning:Analytics},
	file = {arXiv.org Snapshot:/Users/jochenhanisch-johannsen/Zotero/storage/PZ8GASD8/2305.html:text/html;Full Text PDF:/Users/jochenhanisch-johannsen/Zotero/storage/HUJS2QM8/Becerra et al. - 2023 - M2LADS A System for Generating MultiModal Learnin.pdf:application/pdf},
}

@incollection{frasson_culture_2023,
	address = {Cham},
	title = {Culture of {Ethics} in {Adopting} {Learning} {Analytics}},
	volume = {13891},
	isbn = {978-3-031-32882-4 978-3-031-32883-1},
	url = {https://link.springer.com/10.1007/978-3-031-32883-1_52},
	abstract = {Learning analytics (LA) collects, analyzes, and reports large amounts of data about learners in order to improve learning in intelligent tutoring systems. Because LA ethics is an interdisciplinary ﬁeld that addresses moral, legal, and social issues, institutions are responsible for implementing frameworks that address these concerns. Many ethical concerns apply to educational data sets of any size. However, in this study, we concentrate on big data, which increases the scale and granularity of the data collected. We present a synthesis on a growing subject of interest based on ethics regarding the capture of data by LA. This research aims twofold: (a) to extend the review of the scientiﬁc literature on LA ethics issues and (b) to identify emerging trends and answer open-ﬁeld questions discussing three case studies. The following are the research questions for this study: what does LA ethics mean for educational stakeholders, and what are students’ and teachers’ perspectives on ethics as a factor in adopting LA? We developed a multi-stage design process that included a literature review, empirical research, and community involvement. The literature review identiﬁed 68 articles after searching journals and conferences. The selected articles were thoroughly examined using qualitative content analysis. The ﬁndings point to a lack of evidence-based guidelines on data ethics and the need to develop codes of practice to evaluate LA ethics policies. Finally, this work applies an ethical checklist to three case studies as an instructional design model for scholars, policymakers, and instructional designers, so partners can use LA responsibly to improve learning and teaching efﬁcacy.},
	language = {en},
	urldate = {2023-05-27},
	booktitle = {Augmented {Intelligence} and {Intelligent} {Tutoring} {Systems}},
	publisher = {Springer Nature Switzerland},
	author = {Tzimas, Dimitrios and Demetriadis, Stavros},
	editor = {Frasson, Claude and Mylonas, Phivos and Troussas, Christos},
	year = {2023},
	doi = {10.1007/978-3-031-32883-1_52},
	note = {Series Title: Lecture Notes in Computer Science},
	keywords = {Promotion:Literaturanalyse, Charité:Promotion, Leraning:Analytics},
	pages = {591--603},
	file = {Tzimas und Demetriadis - 2023 - Culture of Ethics in Adopting Learning Analytics.pdf:/Users/jochenhanisch-johannsen/Zotero/storage/BF76Z34Z/Tzimas und Demetriadis - 2023 - Culture of Ethics in Adopting Learning Analytics.pdf:application/pdf},
}

@techreport{miglbauer_hochschule_2018,
	address = {Graz},
	type = {Tagungsband},
	title = {Hochschule digital.innovativ},
	shorttitle = {\#{digiPH}},
	language = {de-A},
	editor = {{Verein Forum neue Medien in der Lehre Austria}},
	collaborator = {Miglbauer, Marlene and Kieberl, Lene and Schmid, Stefan},
	year = {2018},
	keywords = {Promotion:Literaturanalyse, Charité:Promotion, Promotion:Literaturanalyse:Berichte},
	pages = {422},
	file = {digiPH_online.pdf:/Users/jochenhanisch-johannsen/Zotero/storage/22WQ6UUF/digiPH_online.pdf:application/pdf},
}

@book{mai_educational_2023,
	address = {Wiesbaden},
	title = {Educational {Data} {Mining} und {Learning} {Analytics}: {Ein} maschinell generierter {Forschungsüberblick}},
	isbn = {978-3-658-39606-0 978-3-658-39607-7},
	shorttitle = {Educational {Data} {Mining} und {Learning} {Analytics}},
	url = {https://link.springer.com/10.1007/978-3-658-39607-7},
	language = {de},
	urldate = {2023-06-21},
	publisher = {Springer Fachmedien Wiesbaden},
	editor = {Mai, Tai Tan and Crane, Martin and Bezbradica, Marija},
	year = {2023},
	doi = {10.1007/978-3-658-39607-7},
	keywords = {Promotion:Literaturanalyse, Charité:Promotion, Leraning:Analytics},
	file = {Mai et al. - 2023 - Educational Data Mining und Learning Analytics Ei.pdf:/Users/jochenhanisch-johannsen/Zotero/storage/2KMNM64Q/Mai et al. - 2023 - Educational Data Mining und Learning Analytics Ei.pdf:application/pdf;Mai et al. - 2023 - Educational Data Mining und Learning Analytics Ei.pdf:/Users/jochenhanisch-johannsen/Zotero/storage/HG63YLZM/Mai et al. - 2023 - Educational Data Mining und Learning Analytics Ei.pdf:application/pdf;Mai et al. - 2023 - Educational Data Mining und Learning Analytics Ei.pdf:/Users/jochenhanisch-johannsen/Zotero/storage/J3I8CUWZ/Mai et al. - 2023 - Educational Data Mining und Learning Analytics Ei.pdf:application/pdf},
}

@techreport{kamp-hartong_auf_2023,
	title = {Auf dem {Weg} zur {Digitalität} in {Schule}},
	url = {https://openhsu.ub.hsu-hh.de/handle/10.24405/14955},
	language = {de},
	urldate = {2023-07-31},
	author = {Kamp-Hartong, Sigrid and Loft-Akhoondi, Anja and Brandau, Nina and Junne, Barbara and Czarnojan, Izabela and Scheytt, Tobias},
	collaborator = {{Helmut-Schmidt-Universität Hamburg} and {Helmut-Schmidt-Universität Hamburg}},
	year = {2023},
	note = {Publisher: Helmut-Schmidt-Universität / Universität der Bundeswehr Hamburg, Forschungscluster OPAL},
	keywords = {Bildung, Promotion:Literaturanalyse, Charité:Promotion, Promotion:Literaturanalyse:Berichte},
	file = {Kamp-Hartong et al. - 2023 - Auf dem Weg zur Digitalität in Schule.pdf:/Users/jochenhanisch-johannsen/Zotero/storage/IYAL4ZY9/Kamp-Hartong et al. - 2023 - Auf dem Weg zur Digitalität in Schule.pdf:application/pdf},
}

@book{heesen_kunstliche_2023,
	address = {Wiesbaden},
	title = {Künstliche {Intelligenz} und {Machine} {Learning} mit {R}: {Anwendungen} im {Bereich} {Business} {Analytics}},
	isbn = {978-3-658-41575-4 978-3-658-41576-1},
	shorttitle = {Künstliche {Intelligenz} und {Machine} {Learning} mit {R}},
	url = {https://link.springer.com/10.1007/978-3-658-41576-1},
	language = {de},
	urldate = {2023-08-03},
	publisher = {Springer Fachmedien Wiesbaden},
	author = {Heesen, Bernd},
	year = {2023},
	doi = {10.1007/978-3-658-41576-1},
	keywords = {Promotion:Literaturanalyse, Charité:Promotion, Leraning:Analytics},
	file = {Heesen - 2023 - Künstliche Intelligenz und Machine Learning mit R.pdf:/Users/jochenhanisch-johannsen/Zotero/storage/YF6ZEPH8/Heesen - 2023 - Künstliche Intelligenz und Machine Learning mit R.pdf:application/pdf},
}

@techreport{ruhland_learning_2023,
	type = {preprint},
	title = {Learning {Analytics} für {Lehrkräfte}},
	url = {https://osf.io/dvkgx},
	abstract = {Learning Analytics supports teachers in dealing with heterogeneous classes and supporting students individually. While teachers from STEM subjects are open-minded, teachers from disciplines that tend to be far removed from technology lack professional access. To enable this, learning analytics is explained from the perspective of pedagogical practice.},
	language = {de},
	urldate = {2023-08-27},
	institution = {EdArXiv},
	author = {Ruhland, Claudia and Schnuecker, Alexander and Shegupta, Ummay and Seegerer, Stefan and Meissner, Roy},
	month = aug,
	year = {2023},
	doi = {10.35542/osf.io/dvkgx},
	keywords = {Promotion:Literaturanalyse, Charité:Promotion, Leraning:Analytics},
	file = {Ruhland et al. - 2023 - Learning Analytics für Lehrkräfte.pdf:/Users/jochenhanisch-johannsen/Zotero/storage/IZJAAHYT/Ruhland et al. - 2023 - Learning Analytics für Lehrkräfte.pdf:application/pdf},
}

@article{hooshyar_learning_2023,
	title = {Learning {Analytics} in {Supporting} {Student} {Agency}: {A} {Systematic} {Review}},
	volume = {15},
	issn = {2071-1050},
	shorttitle = {Learning {Analytics} in {Supporting} {Student} {Agency}},
	url = {https://www.mdpi.com/2071-1050/15/18/13662},
	doi = {10/gsqwd2},
	abstract = {Student agency, or agency for learning, refers to an individual’s ability to act and cause changes during the learning process. Recently, learning analytics (LA) has demonstrated its potential in promoting agency, as it enables students to take an active role in their learning process and supports the development of their self-regulatory skills. Despite the growing interest and potential for supporting student agency, there have yet to be any studies reviewing the extant works dealing with the use of LA in supporting student agency. We systematically reviewed the existing related works in eight major international databases and identiﬁed 15 articles. Analysis of these articles revealed that most of the studies aimed to investigate student or educators’ agency experiences, propose design principles for LA, and to a lesser extent, develop LA methods/dashboards to support agency. Of those studies developing LA, none initially explored student agency experiences and then utilized their ﬁndings to develop evidence-based LA methods and dashboards for supporting student agency. Moreover, we found that the included articles largely rely on descriptive and diagnostic analytics, paying less attention to predictive analytics and completely overlooking the potential of prescriptive learning analytics in supporting agency. Our ﬁndings also shed light on nine key design elements for effective LA support of student agency, including customization, decision-making support, consideration of transparency and privacy, and facilitation of co-design. Surprisingly, we found that no studies have considered the use of LA to support student agency in K–12 education, while higher education has been the focal point of the LA community. Finally, we highlighted the ﬁelds of study and data visualization types that the studies mostly targeted and, more importantly, identiﬁed eight crucial challenges facing LA in its support of student agency.},
	language = {en},
	number = {18},
	urldate = {2023-09-17},
	journal = {Sustainability},
	author = {Hooshyar, Danial and Tammets, Kairit and Ley, Tobias and Aus, Kati and Kollom, Kaire},
	month = sep,
	year = {2023},
	keywords = {Promotion:Literaturanalyse, Charité:Promotion, Leraning:Analytics},
	pages = {13662},
	file = {Hooshyar et al. - 2023 - Learning Analytics in Supporting Student Agency A.pdf:/Users/jochenhanisch-johannsen/Zotero/storage/PNCDRZ9F/Hooshyar et al. - 2023 - Learning Analytics in Supporting Student Agency A.pdf:application/pdf},
}

@article{yang_learning_nodate,
	title = {Learning {Analytics} {Through} {Machine} {Learning} and {Natural} {Language} {Processing}},
	language = {en},
	author = {Yang, Bokai},
	keywords = {⛔ No DOI found, Promotion:Literaturanalyse, Charité:Promotion, Leraning:Analytics},
	file = {Yang - Learning Analytics Through Machine Learning and Na.pdf:/Users/jochenhanisch-johannsen/Zotero/storage/9TD8RYB3/Yang - Learning Analytics Through Machine Learning and Na.pdf:application/pdf},
}

@article{schon_learning_nodate,
	title = {Learning {Analytics} in {Hochschulen} und {Künstliche} {Intelligenz}},
	abstract = {Learning Analytics« ist die Interpretation von Daten, um individuelle Lernprozesse gezielt zu verbessern (Ebner et al., 2013; Greller \& Drachsler, 2012). Learning-AnalyticsAnwendungen geben dabei Empfehlungen, damit Lernende ihr Lernverhalten oder Lehrende das didaktische Setting bzw. die Lehr- und Lernsituation verbessern können. Der Beitrag führt zunächst in Learning Analytics in der Hochschulbildung ein, um dann auf Einsätze von Künstlicher Intelligenz (KI) in der Hochschule überzuführen und Überschneidungen zu identifizieren. Dabei werden vier internationale Beispiele im Themenfeld referiert und vorgestellt (Literatur-/Projektrecherche). Der Beitrag schließt mit einem Ausblick auf Potentiale und Herausforderungen für KI für Learning Analytics in Hochschulen (u.a. Buckingham Shum \& Luckin, 2019).},
	language = {de},
	author = {Schön, Sandra and Leitner, Philipp and Lindner, Jakob and Ebner, Martin},
	keywords = {❓ Multiple DOI, Promotion:Literaturanalyse, Charité:Promotion, Leraning:Analytics},
	file = {Schön et al. - Learning Analytics in Hochschulen und Künstliche I.pdf:/Users/jochenhanisch-johannsen/Zotero/storage/JN6ENL6M/Schön et al. - Learning Analytics in Hochschulen und Künstliche I.pdf:application/pdf},
}

@article{lim_search_2023,
	title = {In {Search} of {Alignment} between {Learning} {Analytics} and {Learning} {Design}: {A} {Multiple} {Case} {Study} in a {Higher} {Education} {Institution}},
	volume = {13},
	issn = {2227-7102},
	shorttitle = {In {Search} of {Alignment} between {Learning} {Analytics} and {Learning} {Design}},
	url = {https://www.mdpi.com/2227-7102/13/11/1114},
	doi = {10/gs4mfh},
	abstract = {Learning design (LD) has increasingly been recognized as a signiﬁcant contextual element for the interpretation and adoption of learning analytics (LA). Yet, few studies have explored how instructors integrate LA feedback into their learning designs, especially within open automated feedback (AF) systems. This research presents a multiple-case study at one higher education institution to unveil instructors’ pilot efforts in using an open AF system to align LA and LD within their unique contexts, with the goal of delivering personalized feedback and tailored support. A notable ﬁnding from these cases is that instructors successfully aligned LA with LD for personalized feedback through checkpoint analytics in highly structured courses. Moreover, they relied on checkpoint analytics as an evaluation mechanism for evaluating impact. Importantly, students perceived a stronger sense of instructors’ support, reinforcing previous ﬁndings on the effectiveness of personalized feedback. This study contributes essential empirical insights to the intersection of learning analytics and learning design, shedding light on practical ways educators align LA and LD for personalized feedback and support.},
	language = {en},
	number = {11},
	urldate = {2023-11-12},
	journal = {Education Sciences},
	author = {Lim, Lisa-Angelique and Atif, Amara and Heggart, Keith and Sutton, Nicole},
	month = nov,
	year = {2023},
	keywords = {Promotion:Literaturanalyse, Charité:Promotion, Leraning:Analytics},
	pages = {1114},
	file = {Lim et al. - 2023 - In Search of Alignment between Learning Analytics .pdf:/Users/jochenhanisch-johannsen/Zotero/storage/5BY7JN92/Lim et al. - 2023 - In Search of Alignment between Learning Analytics .pdf:application/pdf},
}

@article{nuangchalerm_ai-driven_2023,
	title = {{AI}-{Driven} {Learning} {Analytics} in {STEM} {Education}},
	volume = {5},
	abstract = {In recent years, the integration approach of Artificial Intelligence (AI) is called for many disciplines, it also STEM education has paved the way for transformative advancements. This paper provides an example of AI-driven learning analytics within the context of STEM education. It provides a thorough analysis of the AI-driven STEM curriculum and its associated paradigm. Additionally, it highlights the obstacles and possible threats that educators and institutions face when implementing technological innovations in the classroom. The serves as a valuable resource for educators, researchers, and policymakers seeking to harness the power of AI-driven learning analytics to enhance STEM education. The transformative potential of AI is now shaping the future of STEM learning environments while advocating for a responsible and ethical approach to data-driven education. Ethical concerns and moral considerations should be discussed in school AI and STEM education.},
	language = {en},
	number = {2},
	author = {Nuangchalerm, Prasart and Prachagool, Veena},
	year = {2023},
	keywords = {Promotion:Literaturanalyse, Charité:Promotion, Leraning:Analytics},
	file = {Nuangchalerm und Prachagool - 2023 - AI-Driven Learning Analytics in STEM Education.pdf:/Users/jochenhanisch-johannsen/Zotero/storage/HPD399Q4/Nuangchalerm und Prachagool - 2023 - AI-Driven Learning Analytics in STEM Education.pdf:application/pdf},
}

@techreport{bmbf_kompetenzzentren_2023,
	address = {Berlin},
	type = {Fachinformation},
	title = {Kompetenzzentren für digitales und digital gestütztes {Unterrichten} in {Schule} und {Weiterbildung} - {Kompetenzverbund} lernen:digital},
	language = {de},
	institution = {Bundesministerium für Bildung und Forschung (BMBF), Referat Qualitätsförderung Schule},
	editor = {{BMBF}},
	month = nov,
	year = {2023},
	keywords = {Promotion:Literaturanalyse, Charité:Promotion, Promotion:Literaturanalyse:Berichte},
	file = {Kompetenzzentren für digitales und digital gestütz.pdf:/Users/jochenhanisch-johannsen/Zotero/storage/RG2CZ9WU/Kompetenzzentren für digitales und digital gestütz.pdf:application/pdf},
}

@article{lee_ai_2023,
	title = {{AI} in {Education} and {Learning} {Analytics} in {Singapore}: {An} {Overview} of {Key} {Projects} and {Initiatives}},
	volume = {3},
	issn = {2436-1712},
	shorttitle = {{AI} in {Education} and {Learning} {Analytics} in {Singapore}},
	url = {https://www.jstage.jst.go.jp/article/itel/3/1/3_3.1.Inv.p001/_article},
	doi = {10/gs57cn},
	abstract = {Artiﬁcial Intelligence (AI) in education and learning analytics (LA) tools are increasingly being developed and implemented to enhance teaching and learning within Singapore’s education landscape. This paper provides an overview of key AI in education and LA projects and initiatives in Singapore, organized by the types of technology. The identiﬁed projects and initiatives involve a range of techniques and systems to achieve personalized learning, improve student engagement, optimize resources, and also predict student success among a list of educational outcomes. We brieﬂy describe each identiﬁed project before further discussing the collective impact and limitations, as well as the implications for Singapore and her education environments. Overall, this paper seeks to provide an overview of the state and use of AI and LA in education-related projects within Singapore and highlights the need for further research and development in this area to fully realize the potential of these technologies for improvement of teaching and learning.},
	language = {en},
	number = {1},
	urldate = {2023-11-21},
	journal = {Information and Technology in Education and Learning},
	author = {Lee, Alwyn Vwen Yen and Koh, Elizabeth and Looi, Chee Kit},
	year = {2023},
	keywords = {Promotion:Literaturanalyse, Charité:Promotion, Leraning:Analytics},
	pages = {Inv--p001--Inv--p001},
	file = {Lee et al. - 2023 - AI in Education and Learning Analytics in Singapor.pdf:/Users/jochenhanisch-johannsen/Zotero/storage/ZCEJ3M79/Lee et al. - 2023 - AI in Education and Learning Analytics in Singapor.pdf:application/pdf},
}

@article{modak_adaptive_2023,
	title = {Adaptive learning and {Correlative} assessment of differential usage patterns for students with-or-without learning disabilities via learning analytics},
	issn = {2375-4699, 2375-4702},
	url = {https://dl.acm.org/doi/10.1145/3632365},
	doi = {10/gs57cm},
	abstract = {Learning Disabilities (LD) can be categorized into logical, analytical, grammatical, Vocabularyulary, sequential, and inference disabilities. Analysis of such disabilities assists students to identify and strengthen their weak areas. A wide variety of analysis models are proposed by researchers to perform such tasks, but most of these models are highly complex, and cannot be scaled for multimodal parameter sets. To overcome these issues, this text proposes a model for correlative assessment of diferential usage patterns in students with-or-without learning disabilities via multimodal analysis. The proposed model initially collects real-time inference sets for students with Learning Disabilities (LD), and without LDs. These sets consist of question-speciic recorded responses for ‘Addition’, ‘Carry Propagation’, ‘Basic to Advanced Grammar’, ‘Direct, Inference and Vocabulary Comprehension’, ‘Finding odd-man-out’, ‘Sequencing’, and ‘Pseudo and Sight Spelling’ for diferent question sets. Answers to these questions and their metadata were processed via a correlative engine which assisted in evaluation of correctness, time needed per question per category, number of skips, number of revisits, and unanswered ratio for diferent students. This evaluation was combined with temporal analysis in order to identify per-category progress of students. Based on this progress, students were either upgraded to next level, or given lower-level questions, which assisted them to incrementally improve their grades. The model proved that the performance of LD students is 55\% less than the non LD students and an average of 18 LD students have achieved an average of 33\% of improvement after having multiple attempts of the adaptive lessons. The model uses a correlation function, which enables to identify answering patterns of LD and non-LD students with 98.4\% accuracy, thus can be used for clinical scenarios. CCS Concepts: · Computing methodologies → Machine learning; · Information systems → Web data description languages.},
	language = {en},
	urldate = {2023-11-21},
	journal = {ACM Transactions on Asian and Low-Resource Language Information Processing},
	author = {Modak, Masooda M. and Gharpure, Prachi and Sasikumar, M},
	month = nov,
	year = {2023},
	keywords = {Promotion:Literaturanalyse, Charité:Promotion, Leraning:Analytics},
	pages = {3632365},
	file = {Modak et al. - 2023 - Adaptive learning and Correlative assessment of di.pdf:/Users/jochenhanisch-johannsen/Zotero/storage/KKXYF5CK/Modak et al. - 2023 - Adaptive learning and Correlative assessment of di.pdf:application/pdf},
}

@article{bordies_expectations_nodate,
	title = {Expectations {About} {Learning} {Analytics} {After} the {COVID}-19 {Pandemic}: {A} {Study} of 7 {Spanish} {Universities}},
	abstract = {The purpose of this article is to present a study that explores the ideal and predicted expectations of academic staff to learning analytics implementation. The study is focused on teaching staff from seven Spanish universities. Results show that teachers valued positively the learning analytics services as an instrument to facilitate feedback to students, and provide access to accurate data that indicates learning progress, and that the institutions will provide the means to guide teachers in accessing student analytics. The aspect that generates the least interest refers to the obligation to act in support of students whose academic results show poor performance. Results also shows that the expectations observed in Spain are somewhat more optimistic than those observed in previous studies in Europe, but clearly more pessimistic than those conducted in Latinoamerica.},
	language = {en},
	author = {Bordies, Osmel and Muñoz-Merino, Pedro and Martínez-Monés, Alejandra and Dimitriadis-Damoulis, Yannis and Hernández-Leo, Davinia and Álvarez, Ainhoa and Caerio-Rodríguez, Manuel and Cobos, Ruth and Ros, Salvador and Sancho-Vinuesa, Teresa},
	keywords = {Promotion:Literaturanalyse, Charité:Promotion, Leraning:Analytics},
	file = {Bordies et al. - Expectations About Learning Analytics After the CO.pdf:/Users/jochenhanisch-johannsen/Zotero/storage/EIDAUZAG/Bordies et al. - Expectations About Learning Analytics After the CO.pdf:application/pdf},
}

@article{hernandez-leo_chatgpt_nodate,
	title = {{ChatGPT} and {Generative} {AI} in {Higher} {Education}: {User}-{Centered} {Perspectives} and {Implications} for {Learning} {Analytics}},
	abstract = {The increasing availability of easy-to-access generative Artificial Intelligence (AI) tools, like ChatGPT, calls for a need in education to devise new learning scenarios in view of their potential and challenges. Learning Analytics (LA) can play an important role in the understanding and optimization of responsible and reflective uses of AI tools in education. This paper contributes to the exploration of this role, adopting a human-centered perspective. The paper studies the perspectives of professors and students participating in a 'generative AI for learning' training at a public university in Spain. Considering these perspectives, the paper discusses new requirements for learning analytics in these new learning scenarios using AI. The perspectives highlight the potential of these tools as learning assistants, enabling improved use of study time, stimulating creativity, and facilitating personalized feedback. Stakeholders also points out several ethical concerns and risks that may hinder learning. These preliminary results emphasize the need for LA to differentiate between AI-assisted and AI-complement actions and human intelligence at work, aligned with pedagogical intentions. The paper formulates high-level constructs for learning analytics differentiating those actions, illustrated with examples. The paper also discusses that ethical concerns, like student inequality in accessing advanced tools, should be factored into analytics and decision-making tools.},
	language = {en},
	author = {Hernández-Leo, Davinia},
	keywords = {Promotion:Literaturanalyse, Charité:Promotion, GPT, Leraning:Analytics},
	file = {Hernández-Leo - ChatGPT and Generative AI in Higher Education Use.pdf:/Users/jochenhanisch-johannsen/Zotero/storage/VBW2F8BS/Hernández-Leo - ChatGPT and Generative AI in Higher Education Use.pdf:application/pdf},
}

@article{joarder_learning_2023,
	title = {Learning analytics with {iLearn} {Insights}},
	issn = {2653-665X},
	url = {https://publications.ascilite.org/index.php/APUB/article/view/677},
	doi = {10.14742/apubs.2023.677},
	abstract = {ILearn Insights was developed based on four principals of learning analytics design knowledge: integration, agency, reference frame and dialogue (Wise, 2014). It has been observed that targeted visual feedback with clickable links is the most effective way to engage students quickly. The positive impact of iLearn Insights is demonstrated by its rapid uptake by teaching staff across Macquarie University. When it launched in Session 1, 2020, after 18 months of piloting, there were 478 users across 763 units (subjects) who sent 125334 targeted personalised emails. In Session 1, 2023 iLearn Insights was used by approximately 808 users across 40 departments and learning support areas and 1237 units, sending over 316576 targeted emails to encourage students to engage with learning activities or offer support. That represents an increase of 169\% of users, 162\% of units and 253\% of emails in three years. These personalised email exchanges have led to enhanced student engagement, which is critical for student success (Kahu and Nelson, 2018; De Villiers and Werner, 2018; McClenney et al, 2012; Klem and Connell, 2004).},
	language = {en},
	urldate = {2023-12-02},
	journal = {ASCILITE Publications},
	author = {Joarder, Shamim},
	month = nov,
	year = {2023},
	keywords = {Promotion:Literaturanalyse, Charité:Promotion, Leraning:Analytics},
	file = {Joarder - 2023 - Learning analytics with iLearn Insights.pdf:/Users/jochenhanisch-johannsen/Zotero/storage/6X5Q9AZ9/Joarder - 2023 - Learning analytics with iLearn Insights.pdf:application/pdf},
}

@article{corrin_mapping_2023,
	title = {Mapping the connection between {Learning} {Analytics} and {Learning} {Design}},
	issn = {2653-665X},
	url = {https://publications.ascilite.org/index.php/APUB/article/view/480},
	doi = {10.14742/apubs.2023.480},
	abstract = {Over the past decade many have attempted to articulate the connection between Learning Design (LD) and Learning Analytics (LA) in the form of a framework or model. However, there are now so many of these that it is difficult for practitioners to determine which ones are best for which circumstances. In this workshop, participants will be introduced to a new LD/LA map which brings together the key elements from across the multitude of frameworks in order to assist in the operationalisation of learning analytics in higher education. The aim of the workshop is to apply the framework to learning scenarios to evaluate and critique its effectiveness in informing the development of LA systems and interventions. The outcome of the workshop will be a better understanding of the utility of the map and a shared vocabulary relating to how we can talk about the connection of LD and LA in educational environments.},
	language = {en},
	urldate = {2023-12-02},
	journal = {ASCILITE Publications},
	author = {Corrin, Linda and Law, Nancy and Chen, Minghui},
	month = nov,
	year = {2023},
	keywords = {Promotion:Literaturanalyse, Charité:Promotion, Leraning:Analytics},
	file = {Corrin et al. - 2023 - Mapping the connection between Learning Analytics .pdf:/Users/jochenhanisch-johannsen/Zotero/storage/U96LDLWV/Corrin et al. - 2023 - Mapping the connection between Learning Analytics .pdf:application/pdf},
}

@article{dave_missing_nodate,
	title = {The missing link in learning analytics: {A} discussion on using assessment data for student-facing dashboards},
	language = {en},
	author = {Dave, M},
	keywords = {Promotion:Literaturanalyse, Charité:Promotion, Leraning:Analytics},
	file = {Dave - The missing link in learning analytics A discussi.pdf:/Users/jochenhanisch-johannsen/Zotero/storage/XHLZRMZF/Dave - The missing link in learning analytics A discussi.pdf:application/pdf},
}

@article{macfadyen_ikea_2023,
	title = {The “{IKEA} {Model}” for pragmatic development of a custom learning analytics dashboard},
	issn = {2653-665X},
	url = {https://publications.ascilite.org/index.php/APUB/article/view/465},
	doi = {10.14742/apubs.2023.465},
	abstract = {Many educators and learning analytics practitioners find themselves in ‘learning analytics limbo’, with access only to simplistic one-size-fits-all vendor-driven LA dashboards, as they wait for development of possible future LA solutions that would allow customizations that genuinely cater to differences in learning design and educator skills. We present here a simple and pragmatically oriented project that allows individual educators to build and customize an LA solution ‘at home’ with relatively simple tools. This open-source project takes advantage of data available to an educator via the LMS, and allows them to develop and customize an educator-facing dashboard that meets their teaching and learning design needs. This small-scale solution allows local educators and practitioners to continue to build their data literacy and LA-informed teaching skills, and to contribute to ongoing institutional learning through sharing their experience with institutional LA teams.},
	language = {en},
	urldate = {2023-12-02},
	journal = {ASCILITE Publications},
	author = {Macfadyen, Leah P. and Myers, Alison},
	month = nov,
	year = {2023},
	keywords = {Promotion:Literaturanalyse, Charité:Promotion, Leraning:Analytics},
	pages = {482--486},
	file = {Macfadyen und Myers - 2023 - The “IKEA Model” for pragmatic development of a cu.pdf:/Users/jochenhanisch-johannsen/Zotero/storage/NRTKU5ZH/Macfadyen und Myers - 2023 - The “IKEA Model” for pragmatic development of a cu.pdf:application/pdf},
}

@misc{alfredo_human-centred_2023,
	title = {Human-{Centred} {Learning} {Analytics} and {AI} in {Education}: a {Systematic} {Literature} {Review}},
	shorttitle = {Human-{Centred} {Learning} {Analytics} and {AI} in {Education}},
	url = {http://arxiv.org/abs/2312.12751},
	abstract = {The rapid expansion of Learning Analytics (LA) and Artificial Intelligence in Education (AIED) offers new scalable, data-intensive systems but also raises concerns about data privacy and agency. Excluding stakeholders—like students and teachers—from the design process can potentially lead to mistrust and inadequately aligned tools. Despite a shift towards human-centred design in recent LA and AIED research, there remain gaps in our understanding of the importance of human control, safety, reliability, and trustworthiness in the design and implementation of these systems. We conducted a systematic literature review to explore these concerns and gaps. We analysed 108 papers to provide insights about i) the current state of human-centred LA/AIED research; ii) the extent to which educational stakeholders have contributed to the design process of human-centred LA/AIED systems; iii) the current balance between human control and computer automation of such systems; and iv) the extent to which safety, reliability and trustworthiness have been considered in the literature. Results indicate some consideration of human control in LA/AIED system design, but limited end-user involvement in actual design. Based on these findings, we recommend: 1) carefully balancing stakeholders’ involvement in designing and deploying LA/AIED systems throughout all design phases 2) actively involving target end-users, especially students, to delineate the balance between human control and automation, and 3) exploring safety, reliability, and trustworthiness as principles in future human-centred LA/AIED systems.},
	language = {en},
	urldate = {2023-12-23},
	publisher = {arXiv},
	author = {Alfredo, Riordan and Echeverria, Vanessa and Jin, Yueqiao and Yan, Lixiang and Swiecki, Zachari and Gašević, Dragan and Martinez-Maldonado, Roberto},
	month = dec,
	year = {2023},
	note = {arXiv:2312.12751 [cs]},
	keywords = {Computer Science - Computers and Society, Computer Science - Artificial Intelligence, Computer Science - Human-Computer Interaction, Promotion:Literaturanalyse, Charité:Promotion, Leraning:Analytics},
	file = {Alfredo et al. - 2023 - Human-Centred Learning Analytics and AI in Educati.pdf:/Users/jochenhanisch-johannsen/Zotero/storage/KI8UCWVU/Alfredo et al. - 2023 - Human-Centred Learning Analytics and AI in Educati.pdf:application/pdf},
}

@techreport{hofhues_digitale_2023,
	type = {Studienbrief},
	title = {({Digitale}) {Bildungsinfrastrukturen} machen: ({Be}-){Deutungshorizonte} im {Zuge} von {Entwicklungspraktiken} im {Kontext} der {Lehrer}*innen(fort)bildung},
	abstract = {Die vorliegende Studie präsentiert erste Erkenntnisse aus einem Forschungsprojekt, das sich mit dem „Machen” und den „Macher*innen” digitaler Bildungsinfrastrukturen auseinandersetzt. Beantwortet werden soll die Fragestellung, welche Handlungsorientierungen und -praktiken die Arbeit von Macher:innen prägen und wie diese digitale Infrastrukturen formen. Entlang von drei Gruppendiskussionen, die mit „Professionalisierung”, „Userzentrierung” und „Digitaler Katalog” überschrieben sind, rücken vor allem Annahmen und Narrative über Lernen und Bildung, Lern- und Bildungserfolg ebenso wie normative Erwartungshaltungen ins Blickfeld, die mit Lehrer*innen(fort)bildung in engem Zusammenhang stehen. In der Studie wird rekonstruiert, welche Kategorien, Begriffe, Narrative und/oder Bilder das Handlungsfeld von Macher*innen ebenso wie ihre Praktiken ordnen und (vor-)strukturieren.},
	language = {de-DE},
	institution = {FernUniversität in Hagen, Fakultät für Kultur- und Sozialwissenschaften},
	author = {Hofhues, Sandra and Klusemann, Stefan and Gädeke, Eik and Bonnes, Johannes and Goerke, Paula and Weinrebe, Paul and Schütz, Julia},
	editor = {{FernUniversität in Hagen}},
	month = feb,
	year = {2023},
	keywords = {Promotion:todo, Promotion:Literaturanalyse, Charité:Promotion, Promotion:Literaturanalyse:Berichte},
	file = {Hofhues - (Digitale) Bildungsinfrastrukturen machen (Be-)De.pdf:/Users/jochenhanisch-johannsen/Zotero/storage/SV8CXDPP/Hofhues - (Digitale) Bildungsinfrastrukturen machen (Be-)De.pdf:application/pdf},
}

@techreport{miglbauer_hochschullehre_2023,
	address = {Leobersdorf},
	type = {Tagungsband},
	title = {Hochschullehre in großen und kleinen {Gruppen}},
	shorttitle = {\#{digiPH6}},
	abstract = {Bildung kann hier, im Gegensatz zu einem lediglich kumulativen Dazulernen, als eine dynamisch bleibende Veränderung verstanden werden, die Verhältnisse zu anderen, anderem und sich selbst immer wieder in einem neuen Licht erscheinen lässt und damit fortgesetzt qualitativ neue Erfahrungen und deren kritische Betrachtung ermöglicht. Realisierbar wird eine hierzu hilfreiche dialogische Didaktik im präsenzanalogen Als-ob-Raum der Videokonferenz, der Lernenden in (Teil-)Gruppen verschiedene Szenarien des gemeinsamen Erprobens und Erkundens bietet. Lehrende erfahren sich ebenfalls transformatorisch als Mitfragende und Mitproblematisierende. Sie regen Lernende an, Hürden und Hindernisse, Ambivalenzen und Ambiguitäten des digitalen Lernens in der Videokonferenz zu problematisieren, etwa wenn nicht nur kollaborative Lernprozessgestaltungen, sondern auch Steuerungszumutungen per algorithmischer Kontrolle in den Blick geraten. Auf diese Weise avanciert die Videokonferenz zu einem Reflexionsraum, in dem sich Chancen und Grenzen digitaler Bildung resilienzerprobend reflektieren lassen und die hiermit verbundenen Hürden kritisch und kontingenzsensibel problematisiert werden.},
	language = {de-A},
	institution = {Private Pädagogische Hochschule Burgenland},
	author = {Miglbauer, Marlene},
	year = {2023},
	keywords = {Promotion:todo, Promotion:Literaturanalyse, Charité:Promotion, Promotion:Literaturanalyse:Berichte},
	pages = {55},
	file = {Miglbauer - phbhochschulschriften 012023.pdf:/Users/jochenhanisch-johannsen/Zotero/storage/Q7L9QYJJ/Miglbauer - phbhochschulschriften 012023.pdf:application/pdf},
}

@techreport{noauthor_statistic_id1266917_nutzungshaeufigkeit-digitaler-medien-im-unterricht-vor-und-waehrend-corona-krise-2021pdf_nodate,
	title = {statistic\_id1266917\_nutzungshaeufigkeit-digitaler-medien-im-unterricht-vor-und-waehrend-corona-krise-2021.pdf},
	keywords = {Promotion:Literaturanalyse, Charité:Promotion, Promotion:Literaturanalyse:Berichte},
	file = {statistic_id1266917_nutzungshaeufigkeit-digitaler-medien-im-unterricht-vor-und-waehrend-corona-krise-2021.pdf:/Users/jochenhanisch-johannsen/Zotero/storage/MVKCBC6D/statistic_id1266917_nutzungshaeufigkeit-digitaler-medien-im-unterricht-vor-und-waehrend-corona-krise-2021.pdf:application/pdf},
}

@techreport{noauthor_statistic_id203748_umfrage-zur-bedeutung-von-e-learning-anwendungen--unternehmen-2022pdf_nodate,
	title = {statistic\_id203748\_umfrage-zur-bedeutung-von-e-learning-anwendungen-in-unternehmen-2022.pdf},
	keywords = {Promotion:Literaturanalyse, Charité:Promotion, Promotion:Literaturanalyse:Berichte},
	file = {statistic_id203748_umfrage-zur-bedeutung-von-e-learning-anwendungen-in-unternehmen-2022.pdf:/Users/jochenhanisch-johannsen/Zotero/storage/9JRUJ426/statistic_id203748_umfrage-zur-bedeutung-von-e-learning-anwendungen-in-unternehmen-2022.pdf:application/pdf},
}

@techreport{noauthor_mmb-trendmonitor_2022-2023pdf_nodate,
	title = {mmb-{Trendmonitor}\_2022-2023.pdf},
	keywords = {Promotion:Literaturanalyse, Charité:Promotion, Promotion:Literaturanalyse:Berichte},
	file = {mmb-Trendmonitor_2022-2023.pdf:/Users/jochenhanisch-johannsen/Zotero/storage/9JB2CJQM/mmb-Trendmonitor_2022-2023.pdf:application/pdf},
}

@article{gedrimiene_artificial_2024,
	title = {Artificial {Intelligence} ({AI})-enhanced learning analytics ({LA}) for supporting {Career} decisions: advantages and challenges from user perspective},
	volume = {29},
	issn = {1360-2357, 1573-7608},
	shorttitle = {Artificial {Intelligence} ({AI})-enhanced learning analytics ({LA}) for supporting {Career} decisions},
	url = {https://link.springer.com/10.1007/s10639-023-12277-4},
	doi = {10.1007/s10639-023-12277-4},
	abstract = {Artificial intelligence (AI) and learning analytics (LA) tools are increasingly implemented as decision support for learners and professionals. However, their affordances for guidance purposes have yet to be examined. In this paper, we investigated advantages and challenges of AI-enhanced LA tool for supporting career decisions from the user perspective. Participants (N = 106) interacted with the AI-enhanced LA tool and responded to open-ended questionnaire questions. Content analysis was utilized for the data analysis applying two distinct and robust frameworks: technology acceptance model (TAM) and career decision-making model (CDM) as well as looking into user needs. Results indicate that the AI-enhanced LA tool provided five main benefits to the users: provision of career information, research and analysis of the information, diversification of ideas on possible career paths, providing direction and decision support, and self-reflection. The participants perceived the AI-enhanced LA tool as a supportive asset to be used in transitional life situations characterized with uncertainty. Considerable use difficulties were reported as well as need for further diversification of ideas on possible career paths, need for personalization and self-reflection support, and need for further information. Results regarding perceived support for making career decisions showed that CDM elements were unequally supported by the AI-enhanced LA tool. Most support was focused to investigate smaller number of provided options and make decisions, while contextual information was lacking. Implications for career decision making are discussed.},
	language = {en},
	number = {1},
	urldate = {2024-01-10},
	journal = {Education and Information Technologies},
	author = {Gedrimiene, Egle and Celik, Ismail and Kaasila, Antti and Mäkitalo, Kati and Muukkonen, Hanni},
	month = jan,
	year = {2024},
	keywords = {Promotion:Literaturanalyse, Charité:Promotion, Leraning:Analytics},
	pages = {297--322},
	file = {Gedrimiene et al. - 2024 - Artificial Intelligence (AI)-enhanced learning ana.pdf:/Users/jochenhanisch-johannsen/Zotero/storage/QWH9AHG6/Gedrimiene et al. - 2024 - Artificial Intelligence (AI)-enhanced learning ana.pdf:application/pdf},
}

@phdthesis{schmeinck_tag_nodate,
	title = {Tag der mündlichen {Prüfung}:},
	abstract = {Although measures for improving digital media literacy in learning environments have been established since the 1990s, the need for didactically grounded practical examples is still enormous. The subject of primary school sciences takes a particularly important role in that regard, as the living environment of students is an essential characteristic of the subject, and digital media are in fact part of the living environment of most students. In this context, the presented dissertation describes a research project in which digital media implementation, in form of producing audio- and videopodcasts was integrated into school courses while being part of an empirical study. The presented intervention was framed by a quantitative comparative study that questions whether experimental groups differ from each other in terms of subject related knowledge and intrinsic motivation by creating audiopodcasts, videopodcasts or posters during a course about the topic of solubility.},
	language = {de},
	author = {Schmeinck, Dr Daniela},
	keywords = {Promotion:Literaturanalyse, Charité:Promotion},
	file = {Schmeinck - Tag der mündlichen Prüfung.pdf:/Users/jochenhanisch-johannsen/Zotero/storage/QWHD6N7C/Schmeinck - Tag der mündlichen Prüfung.pdf:application/pdf},
}

@phdthesis{ofner_digitale_nodate,
	type = {Master-{Arbeit}},
	title = {Die digitale {Medienabhängigkeit} im {Alltag} von jungen {Erwachsenen}},
	language = {de},
	author = {Ofner, Désirée Jasmin},
	keywords = {Technologieintegration, Promotion:FU5, Promotion:Relevanz:3, Promotion:Weiterführung, Promotion:Literaturanalyse, Charité:Promotion},
	file = {Ofner - Die digitale Medienabhängigkeit im Alltag von jung.pdf:/Users/jochenhanisch-johannsen/Zotero/storage/GSNNDSYZ/Ofner - Die digitale Medienabhängigkeit im Alltag von jung.pdf:application/pdf},
}

@article{roski_learning_2024,
	title = {Learning analytics and the {Universal} {Design} for {Learning} ({UDL}): {A} clustering approach},
	volume = {214},
	issn = {03601315},
	shorttitle = {Learning analytics and the {Universal} {Design} for {Learning} ({UDL})},
	url = {https://linkinghub.elsevier.com/retrieve/pii/S0360131524000423},
	doi = {10.1016/j.compedu.2024.105028},
	language = {en},
	urldate = {2024-03-07},
	journal = {Computers \& Education},
	author = {Roski, Marvin and Sebastian, Ratan and Ewerth, Ralph and Hoppe, Anett and Nehring, Andreas},
	month = jun,
	year = {2024},
	keywords = {Promotion:Literaturanalyse, Charité:Promotion, Leraning:Analytics},
	pages = {105028},
	file = {Roski et al. - 2024 - Learning analytics and the Universal Design for Le.pdf:/Users/jochenhanisch-johannsen/Zotero/storage/XNYPY4K9/Roski et al. - 2024 - Learning analytics and the Universal Design for Le.pdf:application/pdf},
}

@techreport{oellers_kompendium_2024,
	title = {Kompendium: {Didaktische} {Metadaten}},
	copyright = {Creative Commons Attribution 4.0 International},
	shorttitle = {Kompendium},
	url = {https://zenodo.org/doi/10.5281/zenodo.10828758},
	abstract = {Die geäußerten Ansichten und Meinungen sind ausschließlich die der Autoren und spiegeln nicht unbedingt die Ansichten der Europäischen Union oder der Europäischen Kommission wider. Weder die Europäische Union noch die Europäische Kommission können für sie verantwortlich gemacht werden.},
	language = {de},
	urldate = {2024-03-23},
	author = {Oellers, Manuel and Rörtgen, Steffen},
	month = mar,
	year = {2024},
	note = {Publisher: [object Object]
Version Number: 1.0.2},
	keywords = {Bildungsmetadaten, didactical metadata, didaktische Metadaten, E-Learning-Standards, educational metadata, eLearning, metadata standards, Metadatenstandards, pädagogische Metadaten, pedagogical metadata, Promotion:Kerngedanke, Promotion:Literaturanalyse, Charité:Promotion, Promotion:Literaturanalyse:Berichte},
	file = {Oellers und Rörtgen - 2024 - Kompendium Didaktische Metadaten.pdf:/Users/jochenhanisch-johannsen/Zotero/storage/56E28RTY/Oellers und Rörtgen - 2024 - Kompendium Didaktische Metadaten.pdf:application/pdf},
}

@article{cukurova_interplay_nodate,
	title = {The {Interplay} of {Learning}, {Analytics}, and {Artificial} {Intelligence} in {Education}},
	abstract = {This paper presents a multi-dimensional view of AI's role in learning and education, emphasizing the intricate interplay between AI, analytics, and the learning processes. Here, I challenge the prevalent narrow conceptualization of AI as stochastic tools as exemplified in generative AI and argue for the importance of alternative conceptualisations of AI. I highlight the differences between human intelligence and artificial information processing, the “cognitive diversity” inherent in AI algorithms, and posit that AI can also serve as an instrument for understanding human learning. Early learning sciences and AI in Education research, which saw AI as an analogy for human intelligence, have diverged from this perspective, prompting a need to rekindle this connection. The paper presents three unique conceptualizations of AI in education: the externalization of human cognition, the internalization of AI models to influence human mental models, and the extension of human cognition via tightly integrated human-AI systems. Examples from current research and practice are examined as instances of the three conceptualisations, highlighting the potential value and limitations of each conceptualisation for education, as well as the perils of overemphasis on externalising human cognition. It is argued that AI models can be useful as objects to think about learning even though some aspects of learning might just come through the slow experience of living those learning moments and can’t be fully explained with AI models to be hacked with predictions. The paper concludes with advocacy for a broader approach to AI in Education that goes beyond considerations on the design and development of AI solutions in Education, but also includes educating people about AI and innovating educational systems to remain relevant in an AI-ubiquitous world.},
	language = {en},
	author = {Cukurova, Mutlu},
	keywords = {Promotion:Literaturanalyse, Charité:Promotion, Leraning:Analytics},
	file = {Cukurova - The Interplay of Learning, Analytics, and Artifici.pdf:/Users/jochenhanisch-johannsen/Zotero/storage/7Q7AMWDI/Cukurova - The Interplay of Learning, Analytics, and Artifici.pdf:application/pdf},
}

@incollection{de_witt_ki-unterstutzung_2023,
	address = {Wiesbaden},
	title = {{KI}-{Unterstützung} in der {Kulturellen} {Bildung}. {Potenziale} von {Learning} {Analytics} für {Musiklernen} am {Beispiel} automatisierter {Auswertungen} von {Bildschirmaufzeichnungen}},
	isbn = {978-3-658-40078-1 978-3-658-40079-8},
	url = {https://link.springer.com/10.1007/978-3-658-40079-8_18},
	language = {de},
	urldate = {2024-04-03},
	booktitle = {Künstliche {Intelligenz} in der {Bildung}},
	publisher = {Springer Fachmedien Wiesbaden},
	author = {Breiter, Andreas and Krieter, Philipp and Lehmann-Wermser, Andreas and Viertel, Michael and Weyel, Benjamin},
	editor = {De Witt, Claudia and Gloerfeld, Christina and Wrede, Silke Elisabeth},
	year = {2023},
	doi = {10.1007/978-3-658-40079-8_18},
	keywords = {Promotion:Literaturanalyse, Charité:Promotion, Leraning:Analytics},
	pages = {377--392},
	file = {Breiter et al. - 2023 - KI-Unterstützung in der Kulturellen Bildung. Poten.pdf:/Users/jochenhanisch-johannsen/Zotero/storage/CB3HUU5U/Breiter et al. - 2023 - KI-Unterstützung in der Kulturellen Bildung. Poten.pdf:application/pdf},
}

@article{moore_introduction_2024,
	title = {Introduction to special section: learning analytics as part of the higher education ecosystem},
	volume = {36},
	issn = {1042-1726, 1867-1233},
	shorttitle = {Introduction to special section},
	url = {https://link.springer.com/10.1007/s12528-024-09400-w},
	doi = {10.1007/s12528-024-09400-w},
	language = {en},
	number = {1},
	urldate = {2024-05-09},
	journal = {Journal of Computing in Higher Education},
	author = {Moore, Robert L.},
	month = apr,
	year = {2024},
	keywords = {Lehr- und Lerneffektivität, Technologieintegration, Forschungsansätze, Systemanpassung, Datenschutz und IT-Sicherheit, Promotion:Literaturanalyse, Charité:Promotion, Leraning:Analytics},
	pages = {141--144},
	file = {Moore - 2024 - Introduction to special section learning analytic.pdf:/Users/jochenhanisch-johannsen/Zotero/storage/JKYPNRG5/Moore - 2024 - Introduction to special section learning analytic.pdf:application/pdf},
}

@article{motz_wild_2024,
	title = {Wild brooms and learning analytics},
	volume = {36},
	issn = {1042-1726, 1867-1233},
	url = {https://link.springer.com/10.1007/s12528-023-09353-6},
	doi = {10.1007/s12528-023-09353-6},
	abstract = {In this commentary we present an analogy between Johann Wolfgang Von Goethe’s classic poem, The Sorcerer’s Apprentice, and institutional learning analytics. In doing so, we hope to provoke institutions with a simple heuristic when considering their learning analytics initiatives. They might ask themselves, “Are we behaving like the sorcerer’s apprentice?” This would be characterized by initiatives lacking faculty involvement, and we argue that when initiatives fit this pattern, they also lack consideration of their potential hazards, and are likely to fail. We join others in advocating for institutions to, instead, create ecosystems that enable faculty leadership in institutional learning analytics efforts.},
	language = {en},
	number = {1},
	urldate = {2024-05-09},
	journal = {Journal of Computing in Higher Education},
	author = {Motz, Benjamin A. and Morrone, Anastasia S.},
	month = apr,
	year = {2024},
	keywords = {Lehr- und Lerneffektivität, Technologieintegration, Forschungsansätze, Datenschutz und IT-Sicherheit, Promotion:Literaturanalyse, Charité:Promotion, Leraning:Analytics},
	pages = {145--153},
	file = {Motz und Morrone - 2024 - Wild brooms and learning analytics.pdf:/Users/jochenhanisch-johannsen/Zotero/storage/DLSI6SBD/Motz und Morrone - 2024 - Wild brooms and learning analytics.pdf:application/pdf},
}

@article{prinsloo_learning_2024,
	title = {Learning analytics as data ecology: a tentative proposal},
	volume = {36},
	issn = {1042-1726, 1867-1233},
	shorttitle = {Learning analytics as data ecology},
	url = {https://link.springer.com/10.1007/s12528-023-09355-4},
	doi = {10.1007/s12528-023-09355-4},
	abstract = {Central to the institutionalization of learning analytics is the need to understand and improve student learning. Frameworks guiding the implementation of learning analytics flow from and perpetuate specific understandings of learning. Crucially, they also provide insights into how learning analytics acknowledges and positions itself as entangled in institutional data ecosystems, and (increasingly) as part of a data ecology driven by a variety of data interests. The success of learning analytics should therefore be understood in terms of data flows and data interests informing the emerging and mutually constitutive interrelationships and interdependencies between different stakeholders, interests and power relations. This article analyses several selected frameworks to determine the extent to which learning analytics understands itself as a data ecosystem with dynamic interdependencies and interrelationships (human and non-human). Secondly, as learning analytics increasingly becomes part of broader data ecologies, we examine the extent to which learning analytics takes cognizance of the reality, the potential and the risks of being part of a broader data ecology. Finally, this article examines the different data interests vested in learning analytics and critically considers implications for student data sovereignty. The research found that most of the analyzed frameworks understand learning analytics as a data ecosystem, with very little evidence of a broader data ecological understanding. The vast majority of analyzed frameworks consider student data as valuable resource without considering student data ownership and their data rights for self-determination.},
	language = {en},
	number = {1},
	urldate = {2024-05-09},
	journal = {Journal of Computing in Higher Education},
	author = {Prinsloo, Paul and Khalil, Mohammad and Slade, Sharon},
	month = apr,
	year = {2024},
	keywords = {Lehr- und Lerneffektivität, Technologieintegration, Forschungsansätze, Datenschutz und IT-Sicherheit, Promotion:Literaturanalyse, Charité:Promotion, Leraning:Analytics},
	pages = {154--182},
	file = {Prinsloo et al. - 2024 - Learning analytics as data ecology a tentative pr.pdf:/Users/jochenhanisch-johannsen/Zotero/storage/A4CDYPTW/Prinsloo et al. - 2024 - Learning analytics as data ecology a tentative pr.pdf:application/pdf},
}

@article{blackmon_using_2024,
	title = {Using networked learning to improve learning analytics implementation},
	volume = {36},
	issn = {1042-1726, 1867-1233},
	url = {https://link.springer.com/10.1007/s12528-023-09362-5},
	doi = {10.1007/s12528-023-09362-5},
	abstract = {As learning analytics use grows across U.S. colleges and universities, so does the need to discuss the plans, purposes, and paths for the data collected via learning analytics. More specifically, students, faculty, and others who are impacted by learning analytics use should have more information about their campus’ learning analytics practices than many colleges and universities currently provide. Therefore, in the current text, the authors leverage networked learning to create a networked learning analytics logic model that supports colleges and universities in developing more transparent, ethical, inclusive learning analytics plans. The authors build on their previous learning analytics framework as well as extant learning analytics literature to develop the networked learning analytics logic model. The model offers flexibility that allows for adaptive implementation by institutions that are both new to or already engaging in learning analytics initiatives. We encourage those considering learning analytics to implement the model and disseminate their findings so that the model can evolve to align with the dynamic nature of learning analytics implementations.},
	language = {en},
	number = {1},
	urldate = {2024-05-09},
	journal = {Journal of Computing in Higher Education},
	author = {Blackmon, Stephanie J. and Moore, Robert L.},
	month = apr,
	year = {2024},
	keywords = {Lehr- und Lerneffektivität, Technologieintegration, Forschungsansätze, Systemanpassung, Datenschutz und IT-Sicherheit, Promotion:Literaturanalyse, Charité:Promotion, Leraning:Analytics},
	pages = {183--201},
	file = {Blackmon und Moore - 2024 - Using networked learning to improve learning analy.pdf:/Users/jochenhanisch-johannsen/Zotero/storage/H3ITJR6A/Blackmon und Moore - 2024 - Using networked learning to improve learning analy.pdf:application/pdf},
}

@article{gursoy_privacy-preserving_2017,
	title = {Privacy-{Preserving} {Learning} {Analytics}: {Challenges} and {Techniques}},
	volume = {10},
	copyright = {https://ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html},
	issn = {1939-1382},
	shorttitle = {Privacy-{Preserving} {Learning} {Analytics}},
	url = {http://ieeexplore.ieee.org/document/7563858/},
	doi = {10.1109/TLT.2016.2607747},
	abstract = {Educational data contains valuable information that can be harvested through learning analytics to provide new insights for a better education system. However, sharing or analysis of this data introduce privacy risks for the data subjects, mostly students. Existing work in the learning analytics literature identiﬁes the need for privacy and pose interesting research directions, but fails to apply state of the art privacy protection methods with quantiﬁable and mathematically rigorous privacy guarantees. This work aims to employ and evaluate such methods on learning analytics by approaching the problem from two perspectives: (1) the data is anonymized and then shared with a learning analytics expert, and (2) the learning analytics expert is given a privacy-preserving interface that governs her access to the data. We develop proof-of-concept implementations of privacy preserving learning analytics tasks using both perspectives and run them on real and synthetic datasets. We also present an experimental study on the trade-off between individuals’ privacy and the accuracy of the learning analytics tasks.},
	language = {en},
	number = {1},
	urldate = {2024-05-14},
	journal = {IEEE Transactions on Learning Technologies},
	author = {Gursoy, Mehmet Emre and Inan, Ali and Nergiz, Mehmet Ercan and Saygin, Yucel},
	month = jan,
	year = {2017},
	keywords = {Promotion:Literaturanalyse, Charité:Promotion, Leraning:Analytics},
	pages = {68--81},
	file = {Gursoy et al. - 2017 - Privacy-Preserving Learning Analytics Challenges .pdf:/Users/jochenhanisch-johannsen/Zotero/storage/6VPPMIGY/Gursoy et al. - 2017 - Privacy-Preserving Learning Analytics Challenges .pdf:application/pdf},
}

@incollection{sahin_group_2024,
	address = {Cham},
	title = {Group {Cohesion} and {Performance} in {Computer}-{Supported} {Collaborative} {Learning} ({CSCL}): {Using} {Assessment} {Analytics} to {Understand} the {Effects} of {Multi}-attributional {Diversity}},
	isbn = {978-3-031-56364-5 978-3-031-56365-2},
	shorttitle = {Group {Cohesion} and {Performance} in {Computer}-{Supported} {Collaborative} {Learning} ({CSCL})},
	url = {https://link.springer.com/10.1007/978-3-031-56365-2_6},
	language = {en},
	urldate = {2024-05-14},
	booktitle = {Assessment {Analytics} in {Education}},
	publisher = {Springer International Publishing},
	author = {Voltmer, Jan-Bennet and Froehlich, Laura and Reich-Stiebert, Natalia and Raimann, Jennifer and Stürmer, Stefan},
	editor = {Sahin, Muhittin and Ifenthaler, Dirk},
	year = {2024},
	doi = {10.1007/978-3-031-56365-2_6},
	note = {Series Title: Advances in Analytics for Learning and Teaching},
	keywords = {Promotion:Literaturanalyse, Charité:Promotion, Leraning:Analytics},
	pages = {113--132},
	file = {Voltmer et al. - 2024 - Group Cohesion and Performance in Computer-Support.pdf:/Users/jochenhanisch-johannsen/Zotero/storage/REJB3IVX/Voltmer et al. - 2024 - Group Cohesion and Performance in Computer-Support.pdf:application/pdf},
}

@article{razak_learning_2024,
	title = {Learning {Analytics} for {Children}’s using {Augmented} {Reality} {Games}},
	volume = {19},
	abstract = {Data science approaches, which are increasingly used in virtually every sector, can be applied to the quantity of information collected from children’s interactions with augmented reality (AR) games. Data science tools can significantly improve AR game evaluation and help teachers and institutions to make evidence-based decisions. The collection, analysis, and visualisation of player interactions with AR games is referred to as game learning analytics (GLA). The main purpose of this study is to evaluate the effectiveness of children’s performances in AR games by using learning analytics (LA). The data acquired from these analytics can be used to improve AR games, better comprehend player behaviours and strategies, and improve player assessment. To examine the benefits of these technologies, the methods that can be used to implement LA for children in AR games are presented in this paper. The findings suggest that LA provides significant benefits to children while also assisting educators in evaluating students based on their game involvement.},
	language = {en},
	number = {2},
	author = {Razak, Fatin Nasuha Abdul and Kamsin, Amirrudin and Rahman, Hameedur},
	year = {2024},
	keywords = {Promotion:Literaturanalyse, Charité:Promotion, Leraning:Analytics},
	file = {Razak et al. - 2024 - Learning Analytics for Children’s using Augmented .pdf:/Users/jochenhanisch-johannsen/Zotero/storage/JMMURYMK/Razak et al. - 2024 - Learning Analytics for Children’s using Augmented .pdf:application/pdf},
}

@article{shorten_learning_2024,
	title = {Learning analytics and the future of postgraduate medical training},
	issn = {0021-1265, 1863-4362},
	url = {https://link.springer.com/10.1007/s11845-024-03702-9},
	doi = {10.1007/s11845-024-03702-9},
	abstract = {Confronted by the many barriers and deficiencies which currently face those responsible for the training of doctors, the concept of a logic model applied in real time may seem aspirational. However, several of the necessary of logic-based practices are already in place — these include quantified training effect and performance, learning analytics, and applied reflective practice. A nationally or internationally co-ordinated effort is required to harness these disciplines (which currently exist disparately) to create a sustainable and effective training system which is adaptive to its own performance and to society’s changing needs. This will mean making better use of the data currently being generated by and around training, and its presentation in a timely and comprehensible form to the person(s) who is responsible, prepared, and able to use it to best effect.},
	language = {en},
	urldate = {2024-06-02},
	journal = {Irish Journal of Medical Science (1971 -)},
	author = {Shorten, George},
	month = may,
	year = {2024},
	keywords = {Promotion:Literaturanalyse, Charité:Promotion, Leraning:Analytics},
	file = {Shorten - 2024 - Learning analytics and the future of postgraduate .pdf:/Users/jochenhanisch-johannsen/Zotero/storage/ENMGAB7X/Shorten - 2024 - Learning analytics and the future of postgraduate .pdf:application/pdf},
}

@article{thompson_hackathons_nodate,
	title = {Hackathons for {Awareness} and {Community} {Engagement} in {Learning} {Analytics}},
	abstract = {Practitioner Presentation. Challenges of institutional adoption of learning analytics include lack of student engagement, little transparency, and few opportunities for feedback. In this report, we reflect on one element of our institution’s approach to these challenges: regular engagement of students, practitioners, and institutional leadership through Learning Analytics Hackathons. The history and evolution of these hackathons mirror the advancement of learning analytics at the University of British Columbia: they started as community-driven events but have gained institutional support and attention as our data infrastructure and learning analytics culture matures. At the time of writing, the authors are planning the 9th Learning Analytics Hackathon1. In this report, we share our approach, lessons learned, and discuss opportunities for continued student engagement in learning analytics at our institution.},
	language = {en},
	author = {Thompson, Craig and Myers, Alison and Lee, Justin and Engle, Will},
	keywords = {Promotion:Literaturanalyse, Charité:Promotion, Leraning:Analytics},
	file = {Thompson et al. - Hackathons for Awareness and Community Engagement .pdf:/Users/jochenhanisch-johannsen/Zotero/storage/Y2HJVI9K/Thompson et al. - Hackathons for Awareness and Community Engagement .pdf:application/pdf},
}

@techreport{zawacki-richter_zur_2015,
	address = {Berlin},
	type = {Arbeitspapier},
	title = {Zur {Rolle} und {Bedeutung} von digitalen {Medien} in {Internationalisierungsstrategien} deutscher {Hochschulen}},
	language = {de-DE},
	number = {12},
	author = {Zawacki-Richter, Olaf and Bedenlier, Svenja},
	month = sep,
	year = {2015},
	keywords = {Technologieintegration, Systemanpassung, Promotion:FU3, Promotion:Relevanz:3, Promotion:Argumentation, Promotion:Literaturanalyse, Charité:Promotion, Promotion:Literaturanalyse:Berichte},
	pages = {26},
	file = {Zawacki-Richter und Bedenlier - 2015 - ZUR ROLLE UND BEDEUTUNG VON DIGITALEN MEDIEN IN IN.pdf:/Users/jochenhanisch-johannsen/Zotero/storage/DQ8HZRSX/Zawacki-Richter und Bedenlier - 2015 - ZUR ROLLE UND BEDEUTUNG VON DIGITALEN MEDIEN IN IN.pdf:application/pdf},
}

@inproceedings{gorzen_konzept_2023,
	title = {Ein {Konzept} zur {Evaluierung} eines Ökosystems für die {Integration} von {Learning} {Analytics} in {Virtual} {Reality}},
	isbn = {978-3-88579-732-6},
	url = {https://dl.gi.de/handle/20.500.12116/42234},
	doi = {10.18420/DELFI2023-70},
	abstract = {Das Interesse an Lernumgebungen für Virtual Reality hat in den letzten Jahren zugenommen. Eine Möglichkeit, die Effektivität dieser Lernumgebungen messbar zu machen, besteht im Einsatz von Learning Analytics. Allerdings erfordert die Einarbeitung von Programmier*innen umfangreiche Kenntnisse über Learning Analytics und ist häufig aufgrund von Zeitmangel nur oberflächlich. Um Hürden entgegenzuwirken, wurde OmiLAXR (ehemals EduXR) entwickelt, eine vielfältige Unterstützung für Entwickelnde von VR-Applikationen in Unity. Dieser Beitrag stellt kurz Prototypen und ein Evaluationskonzept von Entwicklungsprozessen mit vielfältigem Learning Analytics Ökosystem vor. Ziel ist eine anschließende systematische Anforderungsanalyse für die technische Perspektive auf Learning Analytics Infrastrukturen.},
	language = {de},
	urldate = {2024-06-30},
	publisher = {Gesellschaft für Informatik e.V.},
	author = {Görzen, Sergej and Heinemann, Birte and Schroeder, Ulrik},
	year = {2023},
	keywords = {Automatisierung, Educational Virtual Reality, EduXR, Learning Analytics Infrastruktur, OmiLAXR, Programmierunterstützung, Promotion:Literaturanalyse, Charité:Promotion, Leraning:Analytics},
	file = {Görzen et al. - 2023 - Ein Konzept zur Evaluierung eines Ökosystems für d.pdf:/Users/jochenhanisch-johannsen/Zotero/storage/NBS3DQDN/Görzen et al. - 2023 - Ein Konzept zur Evaluierung eines Ökosystems für d.pdf:application/pdf},
}

@inproceedings{sven_judel_assistenzsystem_nodate,
	title = {Ein {Assistenzsystem} zur {Annotation} von {Learning} {Analytics} {Reports}},
	author = {{Sven Judel} and {Paul Nitzke} and {Ulrik Schroeder}},
	keywords = {Promotion:Literaturanalyse, Charité:Promotion, Leraning:Analytics},
	file = {Judel et al. - 2023 - TextConference Paper.pdf:/Users/jochenhanisch-johannsen/Zotero/storage/ZTZ9VSAK/Judel et al. - 2023 - TextConference Paper.pdf:application/pdf},
}

@article{stamper_learnsphere_2024,
	title = {{LearnSphere}: {A} {Learning} {Data} and {Analytics} {Cyberinfrastructure}},
	volume = {16},
	abstract = {LearnSphere is a web-based data infrastructure designed to transform scientific discovery and innovation in education. It supports learning researchers in addressing a broad range of issues including cognitive, social, and motivational factors in learning, educational content analysis, and educational technology innovation. LearnSphere integrates previously separate educational data and analytic resources developed by participating institutions. The web-based workflow authoring tool, Tigris, allows technical users to contribute sophisticated analytic methods, and learning researchers can adapt and apply those methods using graphical user interfaces, importantly, without additional programming. As part of our use-driven design of LearnSphere, we built a community through workshops and summer schools on educational data mining. Researchers interested in particular student levels or content domains can find student data from elementary through higher-education and across a wide variety of course content such as math, science, computing, and language learning. LearnSphere has facilitated many discoveries about learning, including the importance of active over passive learning activities and the positive association of quality discussion board posts with learning outcomes. LearnSphere also supports research reproducibility, replicability, traceability, and transparency as researchers can share their data and analytic methods along with links to research papers. We demonstrate the capabilities of LearnSphere through a series of case studies that illustrate how analytic components can be combined into research workflow combinations that can be developed and shared. We also show how open web-accessible analytics drive the creation of common formats to streamline repeated analytics and facilitate wider and more flexible dissemination of analytic tool kits.},
	language = {en},
	number = {1},
	author = {Stamper, John and Jr, Philip I Pavlik and Moore, Steven and Koedinger, Kenneth and Rosé, Carolyn P},
	year = {2024},
	keywords = {Promotion:Literaturanalyse, Charité:Promotion, Leraning:Analytics},
	file = {Stamper et al. - 2024 - LearnSphere A Learning Data and Analytics Cyberin.pdf:/Users/jochenhanisch-johannsen/Zotero/storage/VKKKQCNT/Stamper et al. - 2024 - LearnSphere A Learning Data and Analytics Cyberin.pdf:application/pdf},
}

@article{tsai_why_nodate,
	title = {Why {Feedback} {Literacy} {Matters} for {Learning} {Analytics}},
	abstract = {Learning analytics (LA) provides data-driven feedback that aims to improve learning and inform action. For learners, LA-based feedback may scaffold self-regulated learning skills, which are crucial to learning success. For teachers, LA-based feedback may help the evaluation of teaching effects and the need for interventions. However, the current development of LA has presented problems related to the cognitive, social-affective, and structural dimensions of feedback. In light of this, this position paper argues that attention needs to shift from the design of LA as a feedback product to one that facilitates a process in which both teachers and students play active roles in meaning-making. To this end, implications for feedback literacy in the context of LA are discussed.},
	language = {en},
	author = {Tsai, Yi-Shan},
	keywords = {Promotion:Literaturanalyse, Charité:Promotion, Leraning:Analytics},
	file = {Tsai - Why Feedback Literacy Matters for Learning Analyti.pdf:/Users/jochenhanisch-johannsen/Zotero/storage/DCYRD9JE/Tsai - Why Feedback Literacy Matters for Learning Analyti.pdf:application/pdf},
}

@article{alfianti_learning_2024,
	title = {Learning {Analytics} {Approach} to {Improve} {Multiple} {Representation} {Skills} in {Direct}-{Current} {Circuits}},
	abstract = {A high failure rate in physics subjects is caused by students' inability to understand the material. The physics learning process certainly requires an appropriate learning approach. The learning analytics approach is one of the innovations in learning. It allows students to analyze problems. Therefore, this research aimed to determine the learning analytics approach's effect on improving students' multiple representation skills. This research is experimental. The validation of the lesson plan obtained excellent results based on two material experts and four practitioners. Furthermore, the empirical test was carried out on 504 students. The research design was the pretestposttest control group design. The control and experimental groups were determined using cluster random sampling. The findings of this research include (1) the normality test results that are higher than 0.05, which means that the research data was normally distributed. (2) The homogeneity test results were higher than 0.05, which indicated that the data was homogeneous. (3) The paired sample T-test obtained a value lower than 0.05, which means that there was an influence of the multiple representation approach. (4) The N-gain value in the experimental class was higher than the control class. Lastly, (5) only 47.2\% of students used graphical and mathematical representation skills. Based on these findings, the effect of the learning analytics approach on students' multiple representation skills was fairly good with moderate criteria. For further research, learning products or models can be developed to improve multiple representation skills focused on a combination of graphics and mathematics.},
	language = {en},
	author = {Alfianti, Arshi and Kuswanto, Heru},
	year = {2024},
	keywords = {Promotion:Literaturanalyse, Charité:Promotion, Leraning:Analytics},
	file = {Alfianti und Kuswanto - 2024 - Learning Analytics Approach to Improve Multiple Re.pdf:/Users/jochenhanisch-johannsen/Zotero/storage/JQQJN46Q/Alfianti und Kuswanto - 2024 - Learning Analytics Approach to Improve Multiple Re.pdf:application/pdf},
}

@incollection{saqr_why_2024,
	address = {Cham},
	title = {Why {Educational} {Research} {Needs} a {Complex} {System} {Revolution} that {Embraces} {Individual} {Differences}, {Heterogeneity}, and {Uncertainty}},
	isbn = {978-3-031-54463-7 978-3-031-54464-4},
	url = {https://link.springer.com/10.1007/978-3-031-54464-4_22},
	abstract = {Abstract
            Whereas the field of learning analytics has matured, several methodological and theoretical issues remain unresolved. In this chapter, we discuss the potentials of complex systems as an overarching paradigm for understanding the learning process, learners and the learning environments and how they influence learning. We show how using complex system methodologies opens doors for new possibilities that may contribute new knowledge and solve some of the unresolved problems in learning analytics. Furthermore, we unpack the importance of individual differences in advancing the field bringing a much-needed theoretical perspective that could help offer answers to some of our pressing issues.},
	language = {en},
	urldate = {2024-07-02},
	booktitle = {Learning {Analytics} {Methods} and {Tutorials}},
	publisher = {Springer Nature Switzerland},
	author = {Saqr, Mohammed and Schreuder, Marieke J. and López-Pernas, Sonsoles},
	editor = {Saqr, Mohammed and López-Pernas, Sonsoles},
	year = {2024},
	doi = {10.1007/978-3-031-54464-4_22},
	keywords = {Promotion:Literaturanalyse, Charité:Promotion, Leraning:Analytics},
	pages = {723--734},
	file = {Saqr et al. - 2024 - Why Educational Research Needs a Complex System Re.pdf:/Users/jochenhanisch-johannsen/Zotero/storage/IVEQYLMU/Saqr et al. - 2024 - Why Educational Research Needs a Complex System Re.pdf:application/pdf},
}

@incollection{saqr_structural_2024,
	address = {Cham},
	title = {Structural {Equation} {Modeling} with {R} for {Education} {Scientists}},
	isbn = {978-3-031-54463-7 978-3-031-54464-4},
	url = {https://link.springer.com/10.1007/978-3-031-54464-4_21},
	abstract = {Abstract
            Structural Equation Modeling (SEM) is a method for modeling whole sets of complex interrelations between observed and/or latent variables. In its most common form, SEM combines confirmatory factor analysis (CFA with another method named path analysis). Just like CFA, SEM relates observed variables to latent variables that are measured by those observed variables and, as path analysis does, SEM allows for a wide range of regression-type relations between sets of variables (both latent and observed). This chapter presents an introduction to SEM, an integrated strategy for conducting SEM analysis that is well-suited for educational sciences, and a tutorial on how to carry out an SEM analysis in R.},
	language = {en},
	urldate = {2024-07-02},
	booktitle = {Learning {Analytics} {Methods} and {Tutorials}},
	publisher = {Springer Nature Switzerland},
	author = {Jongerling, Joran and López-Pernas, Sonsoles and Saqr, Mohammed and Vogelsmeier, Leonie V. D. E.},
	editor = {Saqr, Mohammed and López-Pernas, Sonsoles},
	year = {2024},
	doi = {10.1007/978-3-031-54464-4_21},
	keywords = {Promotion:Literaturanalyse, Charité:Promotion, Leraning:Analytics},
	pages = {705--721},
	file = {Jongerling et al. - 2024 - Structural Equation Modeling with R for Education .pdf:/Users/jochenhanisch-johannsen/Zotero/storage/2SV6UJVX/Jongerling et al. - 2024 - Structural Equation Modeling with R for Education .pdf:application/pdf},
}

@incollection{saqr_factor_2024,
	address = {Cham},
	title = {Factor {Analysis} in {Education} {Research} {Using} {R}},
	isbn = {978-3-031-54463-7 978-3-031-54464-4},
	url = {https://link.springer.com/10.1007/978-3-031-54464-4_20},
	abstract = {Abstract
            Factor analysis is a method commonly employed to reduce a large number of variables into fewer numbers of factors. The method is often used to identify which observable indicators are representative of latent, not directly-observed constructs. This is a key step in developing valid instruments to assess latent constructs in educational research (e.g., student engagement or motivation). The chapter describes the two main approaches for conducting factor analysis (and how to combine them in an integrated factor analysis strategy) and provides a tutorial on implementing both techniques in the R programming language. The first is confirmatory factor analysis (CFA), a more theory-driven approach, in which a researcher actively specifies the number of underlying constructs as well as the pattern of relations between these dimensions and observed variables. The second is exploratory factor analysis (EFA), a more data-driven approach, in which the number of underlying constructs is inferred from the data, and all underlying constructs are assumed to influence all observed variables (at least to some degree).},
	language = {en},
	urldate = {2024-07-02},
	booktitle = {Learning {Analytics} {Methods} and {Tutorials}},
	publisher = {Springer Nature Switzerland},
	author = {Vogelsmeier, Leonie V. D. E. and Saqr, Mohammed and López-Pernas, Sonsoles and Jongerling, Joran},
	editor = {Saqr, Mohammed and López-Pernas, Sonsoles},
	year = {2024},
	doi = {10.1007/978-3-031-54464-4_20},
	keywords = {Promotion:Literaturanalyse, Charité:Promotion, Leraning:Analytics},
	pages = {673--703},
	file = {Vogelsmeier et al. - 2024 - Factor Analysis in Education Research Using R.pdf:/Users/jochenhanisch-johannsen/Zotero/storage/NZ6P9ZQM/Vogelsmeier et al. - 2024 - Factor Analysis in Education Research Using R.pdf:application/pdf},
}

@incollection{saqr_psychological_2024,
	address = {Cham},
	title = {Psychological {Networks}: {A} {Modern} {Approach} to {Analysis} of {Learning} and {Complex} {Learning} {Processes}},
	isbn = {978-3-031-54463-7 978-3-031-54464-4},
	shorttitle = {Psychological {Networks}},
	url = {https://link.springer.com/10.1007/978-3-031-54464-4_19},
	abstract = {Abstract
            In the examination of psychological phenomena within educational environments, a multitude of variables come into play, and these variables have the potential to interact with, trigger, and exert influence on one another. To grasp the complex dependencies among these variables, investigating the linear associations between each variable pair is not enough. Instead, this complexity demands the application of more advanced techniques that capture the full spectrum of interactions between these variables. One of such techniques is psychological networks. In contrast to social networks, where nodes typically represent individuals and edges signify their interactions or relationships, psychological networks differ in that the nodes represent observed psychological variables, and the edges denote the statistical relationships between them. This chapter serves as an introduction to psychological networks within educational research, offering a tutorial on their estimation, visualization, and interpretation using the R programming language.},
	language = {en},
	urldate = {2024-07-02},
	booktitle = {Learning {Analytics} {Methods} and {Tutorials}},
	publisher = {Springer Nature Switzerland},
	author = {Saqr, Mohammed and Beck, Emorie and López-Pernas, Sonsoles},
	editor = {Saqr, Mohammed and López-Pernas, Sonsoles},
	year = {2024},
	doi = {10.1007/978-3-031-54464-4_19},
	keywords = {Promotion:Literaturanalyse, Charité:Promotion, Leraning:Analytics},
	pages = {639--671},
	file = {Saqr et al. - 2024 - Psychological Networks A Modern Approach to Analy.pdf:/Users/jochenhanisch-johannsen/Zotero/storage/BRNX5Z94/Saqr et al. - 2024 - Psychological Networks A Modern Approach to Analy.pdf:application/pdf},
}

@incollection{saqr_epistemic_2024,
	address = {Cham},
	title = {Epistemic {Network} {Analysis} and {Ordered} {Network} {Analysis} in {Learning} {Analytics}},
	isbn = {978-3-031-54463-7 978-3-031-54464-4},
	url = {https://link.springer.com/10.1007/978-3-031-54464-4_18},
	abstract = {Abstract
            This chapter provides a tutorial on conducting epistemic network analysis (ENA) and ordered network analysis (ONA) using R. We introduce these two techniques together because they share similar theoretical foundations, but each addresses a different challenge for analyzing large-scale qualitative data on learning processes. ENA and ONA are methods for quantifying, visualizing, and interpreting network data. Taking coded data as input, ENA and ONA represent associations between codes in undirected or directed weighted network models, respectively. Both techniques measure the strength of association among codes and illustrate the structure of connections in network graphs, and they quantify changes in the composition and strength of those connections over time. Importantly, ENA and ONA enable comparison of networks both visually and via summary statistics, so they can be used to explore a wide range of research questions in contexts where patterns of association in coded data are hypothesized to be meaningful and where comparing those patterns across individuals or groups is important.},
	language = {en},
	urldate = {2024-07-02},
	booktitle = {Learning {Analytics} {Methods} and {Tutorials}},
	publisher = {Springer Nature Switzerland},
	author = {Tan, Yuanru and Swiecki, Zachari and Ruis, A. R. and Shaffer, David},
	editor = {Saqr, Mohammed and López-Pernas, Sonsoles},
	year = {2024},
	doi = {10.1007/978-3-031-54464-4_18},
	keywords = {Promotion:Literaturanalyse, Charité:Promotion, Leraning:Analytics},
	pages = {569--636},
	file = {Tan et al. - 2024 - Epistemic Network Analysis and Ordered Network Ana.pdf:/Users/jochenhanisch-johannsen/Zotero/storage/E8SSR5C4/Tan et al. - 2024 - Epistemic Network Analysis and Ordered Network Ana.pdf:application/pdf},
}

@incollection{saqr_temporal_2024,
	address = {Cham},
	title = {Temporal {Network} {Analysis}: {Introduction}, {Methods} and {Analysis} with {R}},
	isbn = {978-3-031-54463-7 978-3-031-54464-4},
	shorttitle = {Temporal {Network} {Analysis}},
	url = {https://link.springer.com/10.1007/978-3-031-54464-4_17},
	abstract = {Abstract
            Learning involves relations, interactions and connections between learners, teachers and the world at large. Such interactions are essentially temporal and unfold in time. Yet, researchers have rarely combined the two aspects (the temporal and relational aspects) in an analytics framework. Temporal networks allow modeling of the temporal learning processes i.e., the emergence and flow of activities, communities, and social processes through fine-grained dynamic analysis. This can provide insights into phenomena like knowledge co-construction, information flow, and relationship building. This chapter introduces the basic concepts of temporal networks, their types and techniques. A detailed guide of temporal network analysis is introduced in this chapter, that starts with building the network, visualization, mathematical analysis on the node and graph level. The analysis is performed with a real-world dataset. The discussion chapter offers some extra resources for interested users who want to expand their knowledge of the technique.},
	language = {en},
	urldate = {2024-07-02},
	booktitle = {Learning {Analytics} {Methods} and {Tutorials}},
	publisher = {Springer Nature Switzerland},
	author = {Saqr, Mohammed},
	editor = {Saqr, Mohammed and López-Pernas, Sonsoles},
	year = {2024},
	doi = {10.1007/978-3-031-54464-4_17},
	keywords = {Promotion:Literaturanalyse, Charité:Promotion, Leraning:Analytics},
	pages = {541--567},
	file = {Saqr - 2024 - Temporal Network Analysis Introduction, Methods a.pdf:/Users/jochenhanisch-johannsen/Zotero/storage/CMKJG3VJ/Saqr - 2024 - Temporal Network Analysis Introduction, Methods a.pdf:application/pdf},
}

@incollection{saqr_community_2024,
	address = {Cham},
	title = {Community {Detection} in {Learning} {Networks} {Using} {R}},
	isbn = {978-3-031-54463-7 978-3-031-54464-4},
	url = {https://link.springer.com/10.1007/978-3-031-54464-4_16},
	abstract = {Abstract
            In the field of social network analysis, understanding interactions and group structures takes a center stage. This chapter focuses on finding such groups, constellations or ensembles of actors who can be grouped together, a process often referred to as community detection, particularly in the context of educational research. Community detection aims to uncover tightly knit subgroups of nodes who share strong connectivity within a network or have connectivity patterns that demarcates them from the others. This chapter explores various algorithms and techniques to detect these groups or cohesive clusters. Using well-known R packages, the chapter presents the core approach of identifying and visualizing densely connected subgroups in learning networks.},
	language = {en},
	urldate = {2024-07-02},
	booktitle = {Learning {Analytics} {Methods} and {Tutorials}},
	publisher = {Springer Nature Switzerland},
	author = {Hernández-García, Ángel and Cuenca-Enrique, Carlos and Traxler, Adrienne and López-Pernas, Sonsoles and Conde-González, Miguel Ángel and Saqr, Mohammed},
	editor = {Saqr, Mohammed and López-Pernas, Sonsoles},
	year = {2024},
	doi = {10.1007/978-3-031-54464-4_16},
	keywords = {Promotion:Literaturanalyse, Charité:Promotion, Leraning:Analytics},
	pages = {519--540},
	file = {Hernández-García et al. - 2024 - Community Detection in Learning Networks Using R.pdf:/Users/jochenhanisch-johannsen/Zotero/storage/F6X4AI7S/Hernández-García et al. - 2024 - Community Detection in Learning Networks Using R.pdf:application/pdf},
}

@incollection{saqr_social_2024,
	address = {Cham},
	title = {Social {Network} {Analysis}: {A} {Primer}, a {Guide} and a {Tutorial} in {R}},
	isbn = {978-3-031-54463-7 978-3-031-54464-4},
	shorttitle = {Social {Network} {Analysis}},
	url = {https://link.springer.com/10.1007/978-3-031-54464-4_15},
	abstract = {Abstract
            This chapter introduces the concept and methods of social network analysis (SNA) with a detailed guide to analysis with real world data using the R programming language. The chapter first introduces the basic concepts and types of networks. Then the chapter goes through a detailed step by step analysis of networks, computation of graph level measures as well as centralities with a concise interpretation in a collaborative environment. The chapter concludes with a discussion of network analysis, next steps as well as a list of further readings.},
	language = {en},
	urldate = {2024-07-02},
	booktitle = {Learning {Analytics} {Methods} and {Tutorials}},
	publisher = {Springer Nature Switzerland},
	author = {Saqr, Mohammed and López-Pernas, Sonsoles and Conde-González, Miguel Ángel and Hernández-García, Ángel},
	editor = {Saqr, Mohammed and López-Pernas, Sonsoles},
	year = {2024},
	doi = {10.1007/978-3-031-54464-4_15},
	keywords = {Promotion:Literaturanalyse, Charité:Promotion, Leraning:Analytics},
	pages = {491--518},
	file = {Saqr et al. - 2024 - Social Network Analysis A Primer, a Guide and a T.pdf:/Users/jochenhanisch-johannsen/Zotero/storage/2D5K7J8D/Saqr et al. - 2024 - Social Network Analysis A Primer, a Guide and a T.pdf:application/pdf},
}

@incollection{saqr_modern_2024,
	address = {Cham},
	title = {A {Modern} {Approach} to {Transition} {Analysis} and {Process} {Mining} with {Markov} {Models} in {Education}},
	isbn = {978-3-031-54463-7 978-3-031-54464-4},
	url = {https://link.springer.com/10.1007/978-3-031-54464-4_12},
	abstract = {Abstract
            This chapter presents an introduction to Markovian modelling for the analysis of sequence data. Contrary to the deterministic approach seen in the previous sequence analysis chapters, Markovian models are probabilistic models, focusing on the transitions between states instead of studying sequences as a whole. The chapter provides an introduction to this method and differentiates between its most common variations: first-order Markov models, hidden Markov models, mixture Markov models, and mixture hidden Markov models. In addition to a thorough explanation and contextualisation within the existing literature, the chapter provides a step-by-step tutorial on how to implement each type of Markovian model using the R package seqHMM. The chapter also provides a complete guide to performing stochastic process mining with Markovian models as well as plotting, comparing and clustering different process models.},
	language = {en},
	urldate = {2024-07-02},
	booktitle = {Learning {Analytics} {Methods} and {Tutorials}},
	publisher = {Springer Nature Switzerland},
	author = {Helske, Jouni and Helske, Satu and Saqr, Mohammed and López-Pernas, Sonsoles and Murphy, Keefe},
	editor = {Saqr, Mohammed and López-Pernas, Sonsoles},
	year = {2024},
	doi = {10.1007/978-3-031-54464-4_12},
	keywords = {Promotion:Literaturanalyse, Charité:Promotion, Leraning:Analytics},
	pages = {381--427},
	file = {Helske et al. - 2024 - A Modern Approach to Transition Analysis and Proce.pdf:/Users/jochenhanisch-johannsen/Zotero/storage/YQ63QUNX/Helske et al. - 2024 - A Modern Approach to Transition Analysis and Proce.pdf:application/pdf},
}

@incollection{saqr_introduction_2024,
	address = {Cham},
	title = {An {Introduction} and {R} {Tutorial} to {Model}-{Based} {Clustering} in {Education} via {Latent} {Profile} {Analysis}},
	isbn = {978-3-031-54463-7 978-3-031-54464-4},
	url = {https://link.springer.com/10.1007/978-3-031-54464-4_9},
	abstract = {Abstract
            Heterogeneity has been a hot topic in recent educational literature. Several calls have been voiced to adopt methods that capture different patterns or subgroups within students’ behavior or functioning. Assuming that there is “an average” pattern that represents the entirety of student populations requires the measured construct to have the same causal mechanism, same development pattern, and affect students in exactly the same way. Using a person-centered method (finite Gaussian mixture model or latent profile analysis), the present tutorial shows how to uncover the heterogeneity within engagement data by identifying three latent or unobserved clusters. This chapter offers an introduction to the model-based clustering that includes the principles of the methods, a guide to choice of number of clusters, evaluation of clustering results and a detailed guide with code and a real-life dataset. The discussion elaborates on the interpretation of the results, the advantages of model-based clustering as well as how it compares with other methods.},
	language = {en},
	urldate = {2024-07-02},
	booktitle = {Learning {Analytics} {Methods} and {Tutorials}},
	publisher = {Springer Nature Switzerland},
	author = {Scrucca, Luca and Saqr, Mohammed and López-Pernas, Sonsoles and Murphy, Keefe},
	editor = {Saqr, Mohammed and López-Pernas, Sonsoles},
	year = {2024},
	doi = {10.1007/978-3-031-54464-4_9},
	keywords = {Promotion:Literaturanalyse, Charité:Promotion, Leraning:Analytics},
	pages = {285--317},
	file = {Scrucca et al. - 2024 - An Introduction and R Tutorial to Model-Based Clus.pdf:/Users/jochenhanisch-johannsen/Zotero/storage/AIY3A2MQ/Scrucca et al. - 2024 - An Introduction and R Tutorial to Model-Based Clus.pdf:application/pdf},
}

@incollection{saqr_dissimilarity-based_2024,
	address = {Cham},
	title = {Dissimilarity-{Based} {Cluster} {Analysis} of {Educational} {Data}: {A} {Comparative} {Tutorial} {Using} {R}},
	isbn = {978-3-031-54463-7 978-3-031-54464-4},
	shorttitle = {Dissimilarity-{Based} {Cluster} {Analysis} of {Educational} {Data}},
	url = {https://link.springer.com/10.1007/978-3-031-54464-4_8},
	abstract = {Abstract
            
              Clustering is a collective term which refers to a broad range of techniques aimed at uncovering patterns and subgroups within data. Interest lies in partitioning heterogeneous data into homogeneous groups, whereby cases within a group are more similar to each other than cases assigned to other groups, without foreknowledge of the group labels. Clustering is also an important component of several exploratory methods, analytical techniques, and modelling approaches and therefore has been practiced for decades in education research. In this context, finding patterns or differences among students enables teachers and researchers to improve their understanding of the diversity of students—and their learning processes—and tailor their supports to different needs. This chapter introduces the theory underpinning dissimilarity-based clustering methods. Then, we focus on some of the most widely-used heuristic dissimilarity-based clustering algorithms; namely,
              K
              -means,
              K
              -medoids, and agglomerative hierarchical clustering. The
              K
              -means clustering algorithm is described including the outline of the arguments of the relevant R functions and the main limitations and practical concerns to be aware of in order to obtain the best performance. We also discuss the related
              K
              -medoids algorithm and its own associated concerns and function arguments. We later introduce agglomerative hierarchical clustering and the related R functions while outlining various choices available to practitioners and their implications. Methods for choosing the optimal number of clusters are provided, especially criteria that can guide the choice of clustering solution among multiple competing methodologies—with a particular focus on evaluating solutions obtained using different dissimilarity measures—and not only the choice of the number of clusters
              K
              for a given method. All of these issues are demonstrated in detail with a tutorial in R using a real-life educational data set.},
	language = {en},
	urldate = {2024-07-02},
	booktitle = {Learning {Analytics} {Methods} and {Tutorials}},
	publisher = {Springer Nature Switzerland},
	author = {Murphy, Keefe and López-Pernas, Sonsoles and Saqr, Mohammed},
	editor = {Saqr, Mohammed and López-Pernas, Sonsoles},
	year = {2024},
	doi = {10.1007/978-3-031-54464-4_8},
	keywords = {Promotion:Literaturanalyse, Charité:Promotion, Leraning:Analytics},
	pages = {231--283},
	file = {Murphy et al. - 2024 - Dissimilarity-Based Cluster Analysis of Educationa.pdf:/Users/jochenhanisch-johannsen/Zotero/storage/RZI7QFC8/Murphy et al. - 2024 - Dissimilarity-Based Cluster Analysis of Educationa.pdf:application/pdf},
}

@incollection{saqr_introductory_2024,
	address = {Cham},
	title = {Introductory {Statistics} with {R} for {Educational} {Researchers}},
	isbn = {978-3-031-54463-7 978-3-031-54464-4},
	url = {https://link.springer.com/10.1007/978-3-031-54464-4_5},
	abstract = {Abstract
            Statistics play a fundamental role in learning analytics, providing a means to analyze and make sense of the vast amounts of data generated by learning environments. This chapter provides an introduction to basic statistical concepts using R and covers topics such as measures of central tendency, variability, correlation, and regression analysis. Specifically, readers will learn how to compute descriptive statistics, conduct hypothesis tests, and perform simple linear regression analysis. The chapter also includes practical examples using realistic data sets from the field of learning analytics. By the end of the chapter, readers should have a solid understanding of the basic statistical concepts and methods commonly used in learning analytics, as well as a practical understanding of how to use R to conduct statistical analysis of learning data.},
	language = {en},
	urldate = {2024-07-02},
	booktitle = {Learning {Analytics} {Methods} and {Tutorials}},
	publisher = {Springer Nature Switzerland},
	author = {Tikka, Santtu and Kopra, Juho and Heinäniemi, Merja and López-Pernas, Sonsoles and Saqr, Mohammed},
	editor = {Saqr, Mohammed and López-Pernas, Sonsoles},
	year = {2024},
	doi = {10.1007/978-3-031-54464-4_5},
	keywords = {Promotion:Literaturanalyse, Charité:Promotion, Leraning:Analytics},
	pages = {121--150},
	file = {Tikka et al. - 2024 - Introductory Statistics with R for Educational Res.pdf:/Users/jochenhanisch-johannsen/Zotero/storage/RXFPMEZX/Tikka et al. - 2024 - Introductory Statistics with R for Educational Res.pdf:application/pdf},
}

@incollection{saqr_r_2024,
	address = {Cham},
	title = {An {R} {Approach} to {Data} {Cleaning} and {Wrangling} for {Education} {Research}},
	isbn = {978-3-031-54463-7 978-3-031-54464-4},
	url = {https://link.springer.com/10.1007/978-3-031-54464-4_4},
	abstract = {Abstract
            Data wrangling, also known as data cleaning and preprocessing, is a critical step in the data analysis process, particularly in the context of learning analytics. This chapter provides an introduction to data wrangling using R and covers topics such as data importing, cleaning, manipulation, and reshaping with a focus on tidy data. Specifically, readers will learn how to read data from different file formats (e.g. CSV, Excel), how to manipulate data using the  package, and how to reshape data using the  package. Additionally, the chapter covers techniques for combining multiple data sources. By the end of the chapter, readers should have a solid understanding of how to perform data wrangling tasks in R.},
	language = {en},
	urldate = {2024-07-02},
	booktitle = {Learning {Analytics} {Methods} and {Tutorials}},
	publisher = {Springer Nature Switzerland},
	author = {Kopra, Juho and Tikka, Santtu and Heinäniemi, Merja and López-Pernas, Sonsoles and Saqr, Mohammed},
	editor = {Saqr, Mohammed and López-Pernas, Sonsoles},
	year = {2024},
	doi = {10.1007/978-3-031-54464-4_4},
	keywords = {Promotion:Literaturanalyse, Charité:Promotion, Leraning:Analytics},
	pages = {95--119},
	file = {Kopra et al. - 2024 - An R Approach to Data Cleaning and Wrangling for E.pdf:/Users/jochenhanisch-johannsen/Zotero/storage/TMURTPMY/Kopra et al. - 2024 - An R Approach to Data Cleaning and Wrangling for E.pdf:application/pdf},
}

@incollection{saqr_getting_2024,
	address = {Cham},
	title = {Getting {Started} with {R} for {Education} {Research}},
	isbn = {978-3-031-54463-7 978-3-031-54464-4},
	url = {https://link.springer.com/10.1007/978-3-031-54464-4_3},
	abstract = {Abstract
            The R programming language has become a popular tool for conducting data analysis in the field of learning analytics. This chapter provides an introduction to the basics of R programming, with a focus on the Rstudio integrated development environment and the tidyverse programming paradigm. The chapter covers topics such as data types and structures, control structures, pipes, functions, loops, and input/output operations. By the end of the chapter, readers should have a solid understanding of the basics of R programming and have the tools necessary to learn more in-depth topics such as data wrangling and basic statistics using R.},
	language = {en},
	urldate = {2024-07-02},
	booktitle = {Learning {Analytics} {Methods} and {Tutorials}},
	publisher = {Springer Nature Switzerland},
	author = {Tikka, Santtu and Kopra, Juho and Heinäniemi, Merja and López-Pernas, Sonsoles and Saqr, Mohammed},
	editor = {Saqr, Mohammed and López-Pernas, Sonsoles},
	year = {2024},
	doi = {10.1007/978-3-031-54464-4_3},
	keywords = {Promotion:Literaturanalyse, Charité:Promotion, Leraning:Analytics},
	pages = {67--94},
	file = {Tikka et al. - 2024 - Getting Started with R for Education Research.pdf:/Users/jochenhanisch-johannsen/Zotero/storage/7792KUNV/Tikka et al. - 2024 - Getting Started with R for Education Research.pdf:application/pdf},
}

@incollection{saqr_capturing_2024,
	address = {Cham},
	title = {Capturing the {Wealth} and {Diversity} of {Learning} {Processes} with {Learning} {Analytics} {Methods}},
	isbn = {978-3-031-54463-7 978-3-031-54464-4},
	url = {https://link.springer.com/10.1007/978-3-031-54464-4_1},
	abstract = {Abstract
            The unique position of learning analytics at the intersection of education and computer science while reaching out to several other disciplines such as statistics, psychometrics, econometrics, mathematics, and linguistics has accelerated the growth and expansion of the field. Therefore, it is a crucial endeavor for learning analytics researchers to stay abreast of the latest methodological and computational advances to drive their research forward. The diversity and complexity of the existing methods can make this task overwhelming both for newcomers to the learning analytics field and for experienced researchers. With the motivation to accompany researchers in this challenging journey, the book “Learning Analytics Methods and Tutorials—A Practical Guide Using R” aims to provide a methodological guide for researchers to study, consult, and take the first steps toward innovation in the learning analytics field. Thanks to the unique wealth of authors’ backgrounds and expertise, which include authors of R packages and experts in methods and applications, the book offers a comprehensive array of methods that are described thoroughly with a primer on their usage in prior research in education. These methods include sequence analysis, Markov models, factor analysis, process mining, network analysis, predictive modeling, and cluster analysis among others. A step-by-step tutorial using the R programming language with real-life datasets and case studies is presented for each method. In addition, the initial chapters are devoted to getting novice researchers up to speed with the R programming learners and the basics of data analysis. The present chapter serves as an introduction to the book describing its main aim and intended audience. It describes the structure of the book and the methods covered by each chapter. It also points the readers to the companion code and data repositories to facilitate following the tutorials present in the book chapter.},
	language = {en},
	urldate = {2024-07-02},
	booktitle = {Learning {Analytics} {Methods} and {Tutorials}},
	publisher = {Springer Nature Switzerland},
	author = {López-Pernas, Sonsoles and Misiejuk, Kamila and Kaliisa, Rogers and Conde-González, Miguel Ángel and Saqr, Mohammed},
	editor = {Saqr, Mohammed and López-Pernas, Sonsoles},
	year = {2024},
	doi = {10.1007/978-3-031-54464-4_1},
	keywords = {Promotion:Literaturanalyse, Charité:Promotion, Leraning:Analytics},
	pages = {1--14},
	file = {López-Pernas et al. - 2024 - Capturing the Wealth and Diversity of Learning Pro.pdf:/Users/jochenhanisch-johannsen/Zotero/storage/YCCTEVM6/López-Pernas et al. - 2024 - Capturing the Wealth and Diversity of Learning Pro.pdf:application/pdf},
}

@book{saqr_learning_2024,
	address = {Cham},
	title = {Learning {Analytics} {Methods} and {Tutorials}: {A} {Practical} {Guide} {Using} {R}},
	copyright = {https://creativecommons.org/licenses/by/4.0},
	isbn = {978-3-031-54463-7 978-3-031-54464-4},
	shorttitle = {Learning {Analytics} {Methods} and {Tutorials}},
	url = {https://link.springer.com/10.1007/978-3-031-54464-4},
	language = {en},
	urldate = {2024-07-02},
	publisher = {Springer Nature Switzerland},
	editor = {Saqr, Mohammed and López-Pernas, Sonsoles},
	year = {2024},
	doi = {10.1007/978-3-031-54464-4},
	keywords = {Promotion:Literaturanalyse, Charité:Promotion, Leraning:Analytics},
	file = {Saqr und López-Pernas - 2024 - Learning Analytics Methods and Tutorials A Practi.pdf:/Users/jochenhanisch-johannsen/Zotero/storage/PWU36ALF/Saqr und López-Pernas - 2024 - Learning Analytics Methods and Tutorials A Practi.pdf:application/pdf},
}

@article{mohseni_visual_2024,
	title = {Visual {Learning} {Analytics} for {Educational} {Interventions} in {Primary} and {Secondary} {Schools}: {A} {Scoping} {Review}},
	copyright = {http://creativecommons.org/licenses/by-nc-nd/4.0},
	issn = {1929-7750},
	shorttitle = {Visual {Learning} {Analytics} for {Educational} {Interventions} in {Primary} and {Secondary} {Schools}},
	url = {https://learning-analytics.info/index.php/JLA/article/view/8309},
	doi = {10.18608/jla.2024.8309},
	abstract = {Visual Learning Analytics (VLA) uses analytics to monitor and assess educational data by combining visual and automated analysis to provide educational explanations. Such tools could aid teachers in primary and secondary schools in making pedagogical decisions, however, the evidence of their effectiveness and benefits is still limited. With this scoping review, we provide a comprehensive overview of related research on proposed VLA methods, as well as identifying any gaps in the literature that could assist in describing new and helpful directions to the field. This review searched all relevant articles in five accessible databases — Scopus, Web of Science, ERIC, ACM, and IEEE Xplore — using 40 keywords. These studies were mapped, categorized, and summarized based on their objectives, the collected data, the intervention approaches employed, and the results obtained. The results determined what affordances the VLA tools allowed, what kind of visualizations were used to inform teachers and students, and, more importantly, positive evidence of educational interventions. We conclude that there are moderate-to-clear learning improvements within the limit of the studies’ interventions to support the use of VLA tools. More systematic research is needed to determine whether any learning gains are translated into long-term improvements.},
	language = {en},
	urldate = {2024-07-05},
	journal = {Journal of Learning Analytics},
	author = {Mohseni, Zeynab (Artemis) and Masiello, Italo and Martins, Rafael M. and Nordmark, Susanna},
	month = jun,
	year = {2024},
	keywords = {Promotion:Literaturanalyse, Charité:Promotion, Leraning:Analytics},
	pages = {1--21},
	file = {Mohseni et al. - 2024 - Visual Learning Analytics for Educational Interven.pdf:/Users/jochenhanisch-johannsen/Zotero/storage/ZPWN5SCL/Mohseni et al. - 2024 - Visual Learning Analytics for Educational Interven.pdf:application/pdf},
}

@article{serrano_learning_2024,
	title = {Learning {Analytics} {Dashboards} for {Assessing} {Remote} {Labs} {Users}' {Work}: {A} {Case} {Study} with {VISIR}-{DB}},
	issn = {2211-1662, 2211-1670},
	shorttitle = {Learning {Analytics} {Dashboards} for {Assessing} {Remote} {Labs} {Users}' {Work}},
	url = {https://link.springer.com/10.1007/s10758-024-09752-3},
	doi = {10.1007/s10758-024-09752-3},
	abstract = {In science and engineering education, remote laboratories are designed to bring ubiquity to experimental scenarios, by having real laboratories operated through the Internet. Despite that remote laboratories enable the collection of students’ work data, the educational use of these data is still underdeveloped. Learning analytics dashboards are common tools to present and analyze educational data to provide indicators to understand learning processes. This paper presents how data from remote labs, such as Virtual Instruments Systems In Reality (VISIR), can be analyzed through a learning analytics dashboard to help instructors provide better feedback to their pupils. Visualizations to study the use of the VISIR, to assess students’ performance in a particular activity and to facilitate the assisted assessment of students are introduced to the VISIR dashboard (VISIR-DB). These visualizations include a new recodification of circuits that keeps the fragment being measured, in order to better identify student’s intention. VISIR-DB also incorporates functions to check a priori steps in the resolution process and/or potential errors (observation items), and logical combinations of them to grade students’ performance according to the expected outcomes (assessment milestones). Both work indicators, observation items and assessment milestones, can be defined in activity-specific text files and allow for checking the circuit as coded by the interface, the conceptual circuit it represents, its components, parameters, and measurement result. Main results in the use of VISIR for learning DC circuits course show that students mainly use VISIR when indicated by instructors and a great variability regarding to time of use and number of experiments performed. For the particular assessment activity, VISIR-DB helps to easily detect that there is a significant number of students that did not achieved any of the expected tasks. Additionally, it helps to identify students that still make a huge number of errors at the end of the course. Appropriate interventions can be taken from here.},
	language = {en},
	urldate = {2024-07-07},
	journal = {Technology, Knowledge and Learning},
	author = {Serrano, Vanessa and Cuadros, Jordi and Fernández-Ruano, Laura and García-Zubía, Javier and Hernández-Jayo, Unai and Lluch, Francesc},
	month = jul,
	year = {2024},
	keywords = {Promotion:Literaturanalyse, Charité:Promotion, Leraning:Analytics},
	file = {Serrano et al. - 2024 - Learning Analytics Dashboards for Assessing Remote.pdf:/Users/jochenhanisch-johannsen/Zotero/storage/U56TXIKV/Serrano et al. - 2024 - Learning Analytics Dashboards for Assessing Remote.pdf:application/pdf},
}

@inproceedings{xu_using_2024,
	title = {Using {Learning} {Analytics} in {Understanding} {College} {Students}’ {Behavior} in {ChatGPT}-{Facilitated} {Programming} {Learning}},
	url = {https://repository.isls.org/handle/1/11165},
	doi = {10.22318/icls2024.110098},
	abstract = {As a typical generative artificial intelligence tool, ChatGPT has gained wide attention in its application to programming. However, few researchers have conducted empirical studies to explore how learners use ChatGPT for programming learning. This study analyzes learners' programming learning behaviors to explore the characteristics of different performance learners using ChatGPT-assisted programming learning. The study analyzes the programming process of 15 learners adopting the fine-grained programming behavior method. The results indicate that: (1) Learners regard ChatGPT as a useful programming learning resource and rely on it to guide their learning process. (2) Learners tend to copy debug error messages to ChatGPT and copy its feedback, which may potentially weaken their deep understanding of the code. (3) High-performing group learners use ChatGPT more frequently for programming assistance while low-performing group learners use ChatGPT the least. Based on the findings, this study proposes suggestions for using ChatGPT to assist college students in programming learning.},
	language = {en},
	urldate = {2024-07-07},
	author = {Xu, Jie and Sun, Dan and Li, Yan},
	month = jun,
	year = {2024},
	keywords = {Promotion:Literaturanalyse, Charité:Promotion, GPT, Leraning:Analytics},
	pages = {75--82},
	file = {Xu et al. - 2024 - Using Learning Analytics in Understanding College .pdf:/Users/jochenhanisch-johannsen/Zotero/storage/XPCFFWQJ/Xu et al. - 2024 - Using Learning Analytics in Understanding College .pdf:application/pdf},
}

@phdthesis{esther_sumba_special_2024,
	title = {Special {Focus} on the {Corporate} {Sector} and the {Field} of {Information} {Technology}},
	abstract = {MOOCs have redefined how knowledge is transmitted and acquired. However, as a fairly new form of Educational Technology, there is still a lot of speculation in regards to its impact, future and intellectual integrity within the world of online learning. This exposition will take readers through a journey to explore, analyse and justify the ground breaking disruption of MOOCs as a present day educational technology, its impact on career progression within the corporate sector, future outlook and the various critical issues surrounding Massive Open Online Courses.},
	language = {en},
	author = {{Esther Sumba}},
	year = {2024},
	keywords = {Lehr- und Lerneffektivität, Bewertungsmethoden, Technologieintegration, Promotion:FU4a, Promotion:Relevanz:4, Promotion:Kerngedanke, Promotion:Literaturanalyse, Charité:Promotion},
	file = {Bardone - Special Focus on the Corporate Sector and the Fiel.pdf:/Users/jochenhanisch-johannsen/Zotero/storage/5AJN6BS5/Bardone - Special Focus on the Corporate Sector and the Fiel.pdf:application/pdf},
}

@inproceedings{le_multimedia_2024,
	address = {Phuket Thailand},
	title = {Multimedia learning analytics feedback in simulation-based training: {A} brief review},
	isbn = {979-8-4007-0547-2},
	shorttitle = {Multimedia learning analytics feedback in simulation-based training},
	url = {https://dl.acm.org/doi/10.1145/3643479.3662053},
	doi = {10.1145/3643479.3662053},
	abstract = {Learning analytics has gained significant attention in recent years, particularly in the healthcare field. This area of research offers valuable insights to educators, students, and researchers to enhance the quality of education. One area of focus in learning analytics is how stakeholders provide feedback to each other during training in operating theatres. With the availability of diverse multimedia elements, such as text, images, and spoken language, as data, employing effective feedback methods can bring substantial benefits to teachers, students, and researchers. This study synthesizes various approaches that apply multimedia to provide feedback in teaching, comparing and exploring their potential application in simulation-based medical training. The feasibility of input data, the effectiveness of feedback on recipients, and the AI method of generating or synthesizing feedback using existing data efficiency are also discussed in line with ethical standards. Finally, a multimedia feedback framework is proposed, which utilizes diverse multimedia formats and can be effectively implemented in various realworld scenarios.},
	language = {en},
	urldate = {2024-07-11},
	booktitle = {Proceedings of the 1st {ACM} {Workshop} on {AI}-{Powered} {Q}\&{A} {Systems} for {Multimedia}},
	publisher = {ACM},
	author = {Le, Lai Hoang and Nguyen, Hoang D. and Crane, Martin and Mai, Tai Tan},
	month = jun,
	year = {2024},
	keywords = {Promotion:Literaturanalyse, Charité:Promotion, Leraning:Analytics},
	pages = {25--30},
	file = {Le et al. - 2024 - Multimedia learning analytics feedback in simulati.pdf:/Users/jochenhanisch-johannsen/Zotero/storage/HDP5QVSA/Le et al. - 2024 - Multimedia learning analytics feedback in simulati.pdf:application/pdf},
}

@article{ouyang_ai-driven_2024,
	title = {{AI}-driven learning analytics applications and tools in computer-supported collaborative learning: {A} systematic review},
	volume = {44},
	issn = {1747938X},
	shorttitle = {{AI}-driven learning analytics applications and tools in computer-supported collaborative learning},
	url = {https://linkinghub.elsevier.com/retrieve/pii/S1747938X24000253},
	doi = {10.1016/j.edurev.2024.100616},
	abstract = {Artificial intelligence (AI) has brought new ways for implementing learning analytics in computer-supported collaborative learning (CSCL). However, there is a lack of literature reviews that focus on AI-driven learning analytics applications and tools in CSCL contexts. To fill the gap, this systematic review provides an overview of the goals, characteristics, and effects of existing AI-driven learning analytics applications and tools in CSCL. According to the screening criteria, out of the 2607 initially identified articles between 2004 and 2023, 26 articles are included for final synthesis. Our results show that existing tools primarily focus on students’ cognitive engagement. Existing tools primarily utilize communicative discourse, behavioral, and evaluation data to present results and visualizations. Despite various formats of feedback are provided in existing tools, there is a lack of design principles to guide the tool design and development process. Moreover, although AI techniques have been applied for presenting statistical informa­ tion, there is a lack of providing alert or suggestive information in existing tools or applications. Compared with the positive impacts on collaborative learning, our results indicate a lack of support for instructional interventions in existing tools. This systematic review proposes the following theoretical, technological, and practical implications: (1) the integration of educational and learning theories into AI-driven learning analytics applications and tools; (2) the adoption of advanced AI technologies to collect, analyze, and interpret multi-source and multimodal data; and (3) the support for instructors with actionable suggestions and instructional interventions. Based on our findings, we provide further directions on how to design, analyze, and implement AI-driven learning analytics applications and tools within CSCL contexts.},
	language = {en},
	urldate = {2024-07-14},
	journal = {Educational Research Review},
	author = {Ouyang, Fan and Zhang, Liyin},
	month = aug,
	year = {2024},
	keywords = {Promotion:Literaturanalyse, Charité:Promotion, Leraning:Analytics},
	pages = {100616},
	file = {Ouyang und Zhang - 2024 - AI-driven learning analytics applications and tool.pdf:/Users/jochenhanisch-johannsen/Zotero/storage/3TDCJLBZ/Ouyang und Zhang - 2024 - AI-driven learning analytics applications and tool.pdf:application/pdf},
}

@article{liu_ready_2024,
	title = {Ready or not? {Investigating} in-service teachers’ integration of learning analytics dashboard for assessing students’ collaborative problem solving in {K}–12 classrooms},
	issn = {1360-2357, 1573-7608},
	shorttitle = {Ready or not?},
	url = {https://link.springer.com/10.1007/s10639-024-12842-5},
	doi = {10.1007/s10639-024-12842-5},
	abstract = {Collaborative problem solving (CPS) has emerged as a crucial 21st century competence that benefits students’ studies, future careers, and general well-being, prevailing across disciplines and learning approaches. Given the complex and dynamic nature of CPS, teacher-facing learning analytics dashboards (LADs) have increasingly been adopted to support teachers’ CPS assessments by analysing and visualising various dimensions of students’ CPS. However, there is limited research investigating K-12 teachers’ integration of LADs for CPS assessments in authentic classrooms. In this study, a LAD was implemented to assist K-12 teachers in assessing students’ CPS skills in an educational game. Based on the person-environment fit theory, this study aimed to (1) examine the extent to which teachers’ environmental and personal factors influence LAD usage intention and behaviour and (2) identify personal factors mediating the relationships between environmental factors and LAD usage intention and behaviour. Survey data of 300 in-service teachers from ten Chinese K-12 schools were collected and analysed using partial least squares structural equation modelling (PLS-SEM). Results indicated that our proposed model showed strong in-sample explanatory power and out-of-sample predictive capability. Additionally, subjective norms affected technological pedagogical content knowledge (TPACK) and self-efficacy, while school support affected technostress and self-efficacy. Moreover, subjective norms, technostress, and self-efficacy predicted behavioural intention, while school support, TPACK, and behavioural intention predicted actual behaviour. As for mediation effects, school support indirectly affected behavioural intention through self-efficacy, while subjective norms indirectly affected behavioural intention through self-efficacy and affected actual behaviour through TPACK. This study makes theoretical, methodological, and practical contributions to technology integration in general and LAD implementation in particular.},
	language = {en},
	urldate = {2024-07-15},
	journal = {Education and Information Technologies},
	author = {Liu, Yiming and Hu, Xiao and Ng, Jeremy Tzi Dong and Ma, Zhengyang and Lai, Xiaoyan},
	month = jul,
	year = {2024},
	keywords = {Promotion:Literaturanalyse, Charité:Promotion, Leraning:Analytics},
	file = {Liu et al. - 2024 - Ready or not Investigating in-service teachers’ i.pdf:/Users/jochenhanisch-johannsen/Zotero/storage/L46TTUWQ/Liu et al. - 2024 - Ready or not Investigating in-service teachers’ i.pdf:application/pdf},
}

@phdthesis{ghomi_unterrichten_2023,
	title = {Unterrichten und {Arbeiten} mit digitalen {Medien} – ein {Design}-{Based} {Research} {Ansatz} zur {Gestaltung} einer wirksamen {Lehrkräftefortbildung}},
	language = {de},
	author = {Ghomi, Mina},
	month = aug,
	year = {2023},
	keywords = {Promotion:Literaturanalyse, Charité:Promotion},
	file = {Ghomi - Unterrichten und Arbeiten mit digitalen Medien – e.pdf:/Users/jochenhanisch-johannsen/Zotero/storage/V3QCKKJC/Ghomi - Unterrichten und Arbeiten mit digitalen Medien – e.pdf:application/pdf},
}

@book{salden_learning_2024,
	address = {Wiesbaden},
	series = {Doing {Higher} {Education}},
	title = {Learning {Analytics} und {Künstliche} {Intelligenz} in {Studium} und {Lehre}: {Erfahrungen} und {Schlussfolgerungen} aus einer hochschulweiten {Erprobung}},
	copyright = {https://www.springernature.com/gp/researchers/text-and-data-mining},
	isbn = {978-3-658-42992-8 978-3-658-42993-5},
	shorttitle = {Learning {Analytics} und {Künstliche} {Intelligenz} in {Studium} und {Lehre}},
	url = {https://link.springer.com/10.1007/978-3-658-42993-5},
	language = {de},
	urldate = {2024-07-29},
	publisher = {Springer Fachmedien Wiesbaden},
	editor = {Salden, Peter and Leschke, Jonas},
	year = {2024},
	doi = {10.1007/978-3-658-42993-5},
	keywords = {Promotion:Literaturanalyse, Charité:Promotion, Leraning:Analytics},
	file = {Salden und Leschke - 2024 - Learning Analytics und Künstliche Intelligenz in S.pdf:/Users/jochenhanisch-johannsen/Zotero/storage/DYE3EWCL/Salden und Leschke - 2024 - Learning Analytics und Künstliche Intelligenz in S.pdf:application/pdf},
}

@incollection{salden_zur_2024,
	address = {Wiesbaden},
	title = {Zur {Rolle} von {KI}-{Anwendungen} im {Lernen} und {Lehren} von {Fremdsprachen} im {Hochschulkontext}: {Eine} erste {Bestandsaufnahme} sowie {Entwicklungsmöglichkeiten} aus der {Sicht} eines universitären {Sprachenzentrums}},
	isbn = {978-3-658-42992-8 978-3-658-42993-5},
	shorttitle = {Zur {Rolle} von {KI}-{Anwendungen} im {Lernen} und {Lehren} von {Fremdsprachen} im {Hochschulkontext}},
	url = {https://link.springer.com/10.1007/978-3-658-42993-5_15},
	language = {de},
	urldate = {2024-07-29},
	booktitle = {Learning {Analytics} und {Künstliche} {Intelligenz} in {Studium} und {Lehre}},
	publisher = {Springer Fachmedien Wiesbaden},
	author = {Soltyska, Anna and Berk, Seth and Reich, Astrid},
	editor = {Salden, Peter and Leschke, Jonas},
	year = {2024},
	doi = {10.1007/978-3-658-42993-5_15},
	note = {Series Title: Doing Higher Education},
	keywords = {Promotion:Literaturanalyse, Charité:Promotion, Leraning:Analytics},
	pages = {265--286},
	file = {Soltyska et al. - 2024 - Zur Rolle von KI-Anwendungen im Lernen und Lehren .pdf:/Users/jochenhanisch-johannsen/Zotero/storage/Q8RYFL5V/Soltyska et al. - 2024 - Zur Rolle von KI-Anwendungen im Lernen und Lehren .pdf:application/pdf},
}

@incollection{salden_schreiben_2024,
	address = {Wiesbaden},
	title = {Schreiben mit, ohne oder trotz textgenerierender {Technologien}? {Impulse} aus schreibdidaktischer {Perspektive}},
	isbn = {978-3-658-42992-8 978-3-658-42993-5},
	shorttitle = {Schreiben mit, ohne oder trotz textgenerierender {Technologien}?},
	url = {https://link.springer.com/10.1007/978-3-658-42993-5_14},
	language = {de},
	urldate = {2024-07-29},
	booktitle = {Learning {Analytics} und {Künstliche} {Intelligenz} in {Studium} und {Lehre}},
	publisher = {Springer Fachmedien Wiesbaden},
	author = {Lordick, Nadine},
	editor = {Salden, Peter and Leschke, Jonas},
	year = {2024},
	doi = {10.1007/978-3-658-42993-5_14},
	note = {Series Title: Doing Higher Education},
	keywords = {Promotion:Literaturanalyse, Charité:Promotion, Leraning:Analytics},
	pages = {245--264},
	file = {Lordick - 2024 - Schreiben mit, ohne oder trotz textgenerierender T.pdf:/Users/jochenhanisch-johannsen/Zotero/storage/BRZIMPMH/Lordick - 2024 - Schreiben mit, ohne oder trotz textgenerierender T.pdf:application/pdf},
}

@incollection{salden_data_2024,
	address = {Wiesbaden},
	title = {Data {Literacy} für {Learning} {Analytics}},
	isbn = {978-3-658-42992-8 978-3-658-42993-5},
	url = {https://link.springer.com/10.1007/978-3-658-42993-5_13},
	language = {de},
	urldate = {2024-07-29},
	booktitle = {Learning {Analytics} und {Künstliche} {Intelligenz} in {Studium} und {Lehre}},
	publisher = {Springer Fachmedien Wiesbaden},
	author = {Schwarz, Tabea and Jeworutzki, Sebastian},
	editor = {Salden, Peter and Leschke, Jonas},
	year = {2024},
	doi = {10.1007/978-3-658-42993-5_13},
	note = {Series Title: Doing Higher Education},
	keywords = {Promotion:Literaturanalyse, Charité:Promotion, Leraning:Analytics},
	pages = {225--243},
	file = {Schwarz und Jeworutzki - 2024 - Data Literacy für Learning Analytics.pdf:/Users/jochenhanisch-johannsen/Zotero/storage/GT279T84/Schwarz und Jeworutzki - 2024 - Data Literacy für Learning Analytics.pdf:application/pdf},
}

@incollection{salden_erkenntnisse_2024,
	address = {Wiesbaden},
	title = {Erkenntnisse aus dem {Transferprojekt} ,{Studienberatung}‘},
	isbn = {978-3-658-42992-8 978-3-658-42993-5},
	url = {https://link.springer.com/10.1007/978-3-658-42993-5_12},
	language = {de},
	urldate = {2024-07-29},
	booktitle = {Learning {Analytics} und {Künstliche} {Intelligenz} in {Studium} und {Lehre}},
	publisher = {Springer Fachmedien Wiesbaden},
	author = {Posenau, Jessica and Zeuch, Mark},
	editor = {Salden, Peter and Leschke, Jonas},
	year = {2024},
	doi = {10.1007/978-3-658-42993-5_12},
	note = {Series Title: Doing Higher Education},
	keywords = {Promotion:Literaturanalyse, Charité:Promotion, Leraning:Analytics},
	pages = {205--224},
	file = {Posenau und Zeuch - 2024 - Erkenntnisse aus dem Transferprojekt ,Studienberat.pdf:/Users/jochenhanisch-johannsen/Zotero/storage/4AHJEK86/Posenau und Zeuch - 2024 - Erkenntnisse aus dem Transferprojekt ,Studienberat.pdf:application/pdf},
}

@incollection{salden_didaktische_2024,
	address = {Wiesbaden},
	title = {Didaktische {Perspektiven} auf {Learning} {Analytics} in der {Hochschulbildung}},
	isbn = {978-3-658-42992-8 978-3-658-42993-5},
	url = {https://link.springer.com/10.1007/978-3-658-42993-5_11},
	language = {de},
	urldate = {2024-07-29},
	booktitle = {Learning {Analytics} und {Künstliche} {Intelligenz} in {Studium} und {Lehre}},
	publisher = {Springer Fachmedien Wiesbaden},
	author = {Leschke, Jonas and Salden, Peter},
	editor = {Salden, Peter and Leschke, Jonas},
	year = {2024},
	doi = {10.1007/978-3-658-42993-5_11},
	note = {Series Title: Doing Higher Education},
	keywords = {Promotion:Literaturanalyse, Charité:Promotion, Leraning:Analytics},
	pages = {187--204},
	file = {Leschke und Salden - 2024 - Didaktische Perspektiven auf Learning Analytics in.pdf:/Users/jochenhanisch-johannsen/Zotero/storage/9E96EMB3/Leschke und Salden - 2024 - Didaktische Perspektiven auf Learning Analytics in.pdf:application/pdf},
}

@incollection{salden_learning_2024-1,
	address = {Wiesbaden},
	title = {Learning {Analytics}-{Policys} im {Hochschulkontext}},
	isbn = {978-3-658-42992-8 978-3-658-42993-5},
	url = {https://link.springer.com/10.1007/978-3-658-42993-5_10},
	language = {de},
	urldate = {2024-07-29},
	booktitle = {Learning {Analytics} und {Künstliche} {Intelligenz} in {Studium} und {Lehre}},
	publisher = {Springer Fachmedien Wiesbaden},
	author = {Scheffel, Maren and Simis, Christos and Leschke, Jonas and Borgards, Lena and Salden, Peter},
	editor = {Salden, Peter and Leschke, Jonas},
	year = {2024},
	doi = {10.1007/978-3-658-42993-5_10},
	note = {Series Title: Doing Higher Education},
	keywords = {Promotion:Literaturanalyse, Charité:Promotion, Leraning:Analytics},
	pages = {169--185},
	file = {Scheffel et al. - 2024 - Learning Analytics-Policys im Hochschulkontext.pdf:/Users/jochenhanisch-johannsen/Zotero/storage/AV7AFRDL/Scheffel et al. - 2024 - Learning Analytics-Policys im Hochschulkontext.pdf:application/pdf},
}

@incollection{salden_hochschulen_2024,
	address = {Wiesbaden},
	title = {Hochschulen im {Spannungsfeld} zwischen {Datenschutz} und {Learning} {Analytics} – eine {Analyse} der {Konflikte} und aktuelle {Lösungsansätze}},
	isbn = {978-3-658-42992-8 978-3-658-42993-5},
	url = {https://link.springer.com/10.1007/978-3-658-42993-5_9},
	language = {de},
	urldate = {2024-07-29},
	booktitle = {Learning {Analytics} und {Künstliche} {Intelligenz} in {Studium} und {Lehre}},
	publisher = {Springer Fachmedien Wiesbaden},
	author = {Lentzsch, Christopher and Loser, Kai-Uwe},
	editor = {Salden, Peter and Leschke, Jonas},
	year = {2024},
	doi = {10.1007/978-3-658-42993-5_9},
	note = {Series Title: Doing Higher Education},
	keywords = {Promotion:Literaturanalyse, Charité:Promotion, Leraning:Analytics},
	pages = {149--167},
	file = {Lentzsch und Loser - 2024 - Hochschulen im Spannungsfeld zwischen Datenschutz .pdf:/Users/jochenhanisch-johannsen/Zotero/storage/W2B6SVUE/Lentzsch und Loser - 2024 - Hochschulen im Spannungsfeld zwischen Datenschutz .pdf:application/pdf},
}

@incollection{salden_ki-gestutzte_2024,
	address = {Wiesbaden},
	title = {{KI}-gestützte {Learning} {Analytics}: {Geschenk} oder {Falle} für die {Hochschulbildung}? {Ein} ethischer {Exkurs}},
	isbn = {978-3-658-42992-8 978-3-658-42993-5},
	shorttitle = {{KI}-gestützte {Learning} {Analytics}},
	url = {https://link.springer.com/10.1007/978-3-658-42993-5_8},
	language = {de},
	urldate = {2024-07-29},
	booktitle = {Learning {Analytics} und {Künstliche} {Intelligenz} in {Studium} und {Lehre}},
	publisher = {Springer Fachmedien Wiesbaden},
	author = {Simis, Christos and Weydner-Volkmann, Sebastian},
	editor = {Salden, Peter and Leschke, Jonas},
	year = {2024},
	doi = {10.1007/978-3-658-42993-5_8},
	note = {Series Title: Doing Higher Education},
	keywords = {Promotion:Literaturanalyse, Charité:Promotion, Leraning:Analytics},
	pages = {127--148},
	file = {Simis und Weydner-Volkmann - 2024 - KI-gestützte Learning Analytics Geschenk oder Fal.pdf:/Users/jochenhanisch-johannsen/Zotero/storage/CLZ5WN8L/Simis und Weydner-Volkmann - 2024 - KI-gestützte Learning Analytics Geschenk oder Fal.pdf:application/pdf},
}

@incollection{salden_kiedunrw_2024,
	address = {Wiesbaden},
	title = {{KI}:edu.nrw – eine {Betrachtung} aus der {Perspektive} des {Teilprojektes} {Technik}},
	isbn = {978-3-658-42992-8 978-3-658-42993-5},
	shorttitle = {{KI}},
	url = {https://link.springer.com/10.1007/978-3-658-42993-5_7},
	language = {de},
	urldate = {2024-07-29},
	booktitle = {Learning {Analytics} und {Künstliche} {Intelligenz} in {Studium} und {Lehre}},
	publisher = {Springer Fachmedien Wiesbaden},
	author = {Metzger, Christian and Bovermann, Martin},
	editor = {Salden, Peter and Leschke, Jonas},
	year = {2024},
	doi = {10.1007/978-3-658-42993-5_7},
	note = {Series Title: Doing Higher Education},
	keywords = {Promotion:Literaturanalyse, Charité:Promotion, Leraning:Analytics},
	pages = {109--126},
	file = {Metzger und Bovermann - 2024 - KIedu.nrw – eine Betrachtung aus der Perspektive .pdf:/Users/jochenhanisch-johannsen/Zotero/storage/EW6V4PY7/Metzger und Bovermann - 2024 - KIedu.nrw – eine Betrachtung aus der Perspektive .pdf:application/pdf},
}

@incollection{salden_learning_2024-2,
	address = {Wiesbaden},
	title = {Learning {Analytics} und {Künstliche} {Intelligenz} in der medizinischen {Ausbildung}},
	isbn = {978-3-658-42992-8 978-3-658-42993-5},
	url = {https://link.springer.com/10.1007/978-3-658-42993-5_6},
	language = {de},
	urldate = {2024-07-29},
	booktitle = {Learning {Analytics} und {Künstliche} {Intelligenz} in {Studium} und {Lehre}},
	publisher = {Springer Fachmedien Wiesbaden},
	author = {Schäfer, Thorsten and Ruschke, Philipp},
	editor = {Salden, Peter and Leschke, Jonas},
	year = {2024},
	doi = {10.1007/978-3-658-42993-5_6},
	note = {Series Title: Doing Higher Education},
	keywords = {Promotion:Literaturanalyse, Charité:Promotion, Leraning:Analytics},
	pages = {97--106},
	file = {Schäfer und Ruschke - 2024 - Learning Analytics und Künstliche Intelligenz in d.pdf:/Users/jochenhanisch-johannsen/Zotero/storage/7IJEZGTP/Schäfer und Ruschke - 2024 - Learning Analytics und Künstliche Intelligenz in d.pdf:application/pdf},
}

@incollection{salden_enhancing_2024,
	address = {Wiesbaden},
	title = {Enhancing {Learning} {Experiences} in {Sports} {Science} through {Video} and {AI}-generated {Feedback}},
	isbn = {978-3-658-42992-8 978-3-658-42993-5},
	url = {https://link.springer.com/10.1007/978-3-658-42993-5_5},
	abstract = {In sports and exercise, establishing new and optimizing already learned movements paves the way towards an athlete’s success. This process of learning repeatedly relies on human interaction and therefore requires interactions between athletes, coaches, or teachers. To reduce yet not eliminate this dependency, Artiﬁcial Intelligence (AI) may be able to provide support and qualitative feedback to athletes, while they keep working on movement perfection themselves.},
	language = {en},
	urldate = {2024-07-29},
	booktitle = {Learning {Analytics} und {Künstliche} {Intelligenz} in {Studium} und {Lehre}},
	publisher = {Springer Fachmedien Wiesbaden},
	author = {Venzke, Jan and Hohmann, Richard and Krombholz, Arno and Platen, Petra and Reichert, Markus},
	editor = {Salden, Peter and Leschke, Jonas},
	year = {2024},
	doi = {10.1007/978-3-658-42993-5_5},
	note = {Series Title: Doing Higher Education},
	keywords = {Promotion:Literaturanalyse, Charité:Promotion, Leraning:Analytics},
	pages = {79--95},
	file = {Venzke et al. - 2024 - Enhancing Learning Experiences in Sports Science t.pdf:/Users/jochenhanisch-johannsen/Zotero/storage/5W3EAVVB/Venzke et al. - 2024 - Enhancing Learning Experiences in Sports Science t.pdf:application/pdf},
}

@incollection{salden_learning_2024-3,
	address = {Wiesbaden},
	title = {Learning {Analytics} in {Mathematiklehrveranstaltungen} – adaptive und interaktive {Handlungsempfehlungen} in {Dashboards}},
	isbn = {978-3-658-42992-8 978-3-658-42993-5},
	url = {https://link.springer.com/10.1007/978-3-658-42993-5_4},
	language = {de},
	urldate = {2024-07-29},
	booktitle = {Learning {Analytics} und {Künstliche} {Intelligenz} in {Studium} und {Lehre}},
	publisher = {Springer Fachmedien Wiesbaden},
	author = {Kallweit, Michael and Rolka, Katrin},
	editor = {Salden, Peter and Leschke, Jonas},
	year = {2024},
	doi = {10.1007/978-3-658-42993-5_4},
	note = {Series Title: Doing Higher Education},
	keywords = {Promotion:Literaturanalyse, Charité:Promotion, Leraning:Analytics},
	pages = {63--77},
	file = {Kallweit und Rolka - 2024 - Learning Analytics in Mathematiklehrveranstaltunge.pdf:/Users/jochenhanisch-johannsen/Zotero/storage/JUMQDWGT/Kallweit und Rolka - 2024 - Learning Analytics in Mathematiklehrveranstaltunge.pdf:application/pdf},
}

@incollection{salden_learning_2024-4,
	address = {Wiesbaden},
	title = {Learning {Analytics} in der {Erziehungswissenschaft}: {Lerndatenbasierte} {Förderung} von {Selbstregulation} in einem {Statistikkurs}},
	isbn = {978-3-658-42992-8 978-3-658-42993-5},
	shorttitle = {Learning {Analytics} in der {Erziehungswissenschaft}},
	url = {https://link.springer.com/10.1007/978-3-658-42993-5_3},
	language = {de},
	urldate = {2024-07-29},
	booktitle = {Learning {Analytics} und {Künstliche} {Intelligenz} in {Studium} und {Lehre}},
	publisher = {Springer Fachmedien Wiesbaden},
	author = {Radtke, Anna and Osinski, Meike and Serova, Katja and Scheffel, Maren and Rummel, Nikol},
	editor = {Salden, Peter and Leschke, Jonas},
	year = {2024},
	doi = {10.1007/978-3-658-42993-5_3},
	note = {Series Title: Doing Higher Education},
	keywords = {Promotion:Literaturanalyse, Charité:Promotion, Leraning:Analytics},
	pages = {45--61},
	file = {Radtke et al. - 2024 - Learning Analytics in der Erziehungswissenschaft .pdf:/Users/jochenhanisch-johannsen/Zotero/storage/UJYP7G4T/Radtke et al. - 2024 - Learning Analytics in der Erziehungswissenschaft .pdf:application/pdf},
}

@incollection{salden_empowering_2024,
	address = {Wiesbaden},
	title = {Empowering {Advisors}: {Designing} a {Dashboard} for {University} {Student} {Guidance}},
	isbn = {978-3-658-42992-8 978-3-658-42993-5},
	shorttitle = {Empowering {Advisors}},
	url = {https://link.springer.com/10.1007/978-3-658-42993-5_2},
	abstract = {Academic advising is a critical factor in course planning, which is aimed at supporting students in achieving their individual goals. Many academic advisors inefﬁciently interpret raw data from students and courses to make recommendations. This chapter introduces a signiﬁcant advancement: a dashboard for academic advisors that simpliﬁes this process through interactive visualizations of student and course data. Going beyond the existing advisory process, the application of item response theory enables the valid and reliable modeling of course difﬁculties and the probabilities of individual students passing. This innovation not only allows the advising process to make tailored predictions for individual students but also actively monitors the difﬁculty of courses over time, adding an analytical dimension that was previously unattainable. Highlighting the dashboard’s potential to enhance student success, the tool aims to streamline advisory processes. The dashboard was tested with data from a Computer Science major and her advisor. Interviews with the academic advisor indicate that this use of data, augmented by the application of item response theory, equips advisors with a robust decision-making tool that beneﬁts both students and higher education institutions.},
	language = {en},
	urldate = {2024-07-29},
	booktitle = {Learning {Analytics} und {Künstliche} {Intelligenz} in {Studium} und {Lehre}},
	publisher = {Springer Fachmedien Wiesbaden},
	author = {Baucks, Frederik and Wiskott, Laurenz},
	editor = {Salden, Peter and Leschke, Jonas},
	year = {2024},
	doi = {10.1007/978-3-658-42993-5_2},
	note = {Series Title: Doing Higher Education},
	keywords = {Promotion:Literaturanalyse, Charité:Promotion, Leraning:Analytics},
	pages = {27--44},
	file = {Baucks und Wiskott - 2024 - Empowering Advisors Designing a Dashboard for Uni.pdf:/Users/jochenhanisch-johannsen/Zotero/storage/HBWK9DC6/Baucks und Wiskott - 2024 - Empowering Advisors Designing a Dashboard for Uni.pdf:application/pdf},
}

@incollection{salden_projekt_2024,
	address = {Wiesbaden},
	title = {Das {Projekt} {KI}:edu.nrw – {Rückblick} für einen {Ausblick}},
	isbn = {978-3-658-42992-8 978-3-658-42993-5},
	shorttitle = {Das {Projekt} {KI}},
	url = {https://link.springer.com/10.1007/978-3-658-42993-5_1},
	language = {de},
	urldate = {2024-07-29},
	booktitle = {Learning {Analytics} und {Künstliche} {Intelligenz} in {Studium} und {Lehre}},
	publisher = {Springer Fachmedien Wiesbaden},
	author = {Salden, Peter and Leschke, Jonas and Persike, Malte},
	editor = {Salden, Peter and Leschke, Jonas},
	year = {2024},
	doi = {10.1007/978-3-658-42993-5_1},
	note = {Series Title: Doing Higher Education},
	keywords = {Promotion:Literaturanalyse, Charité:Promotion, Leraning:Analytics},
	pages = {3--24},
	file = {Salden et al. - 2024 - Das Projekt KIedu.nrw – Rückblick für einen Ausbl.pdf:/Users/jochenhanisch-johannsen/Zotero/storage/42AJR65N/Salden et al. - 2024 - Das Projekt KIedu.nrw – Rückblick für einen Ausbl.pdf:application/pdf},
}

@article{wasson_implementing_2024,
	title = {Implementing {Learning} {Analytics} in {Norway}: {Four} {Central} {Dilemmas}},
	copyright = {http://creativecommons.org/licenses/by-nc-nd/4.0},
	issn = {1929-7750},
	shorttitle = {Implementing {Learning} {Analytics} in {Norway}},
	url = {https://learning-analytics.info/index.php/JLA/article/view/8241},
	doi = {10.18608/jla.2024.8241},
	abstract = {In June 2022, the Norwegian Expert Commission on Learning Analytics delivered an interim report to the Norwegian Minister of Education and Research. Motivated by the need to establish a solid foundation upon which to regulate and promote the use of learning analytics in the Norwegian educational sector, the Ministry asked the Expert Commission to investigate the relevant pedagogical, ethical, legal, and privacy issues. Addressing primary, secondary, higher, and vocational education, the interim report surveys the field of learning analytics and the regulatory environment across the contexts and analyzes its challenges and opportunities for Norwegian education. Four dilemmas — data, learning, governance, and competence — signal where greater knowledge, awareness, and reflection are needed, as well as the nature of necessary policy and regulatory choices. In this practical report, we offer insights on the use, development, and regulation of LA in different countries, describe the Expert Commission mandate, work method, and dilemmas, and conclude with a reflection on the relationship between research on learning analytics and the challenges that arise when implementing learning analytics in practice. This practical report is relevant for those interested in developing policies or practices surrounding the use of learning analytics at the local or national level.},
	language = {en},
	urldate = {2024-08-02},
	journal = {Journal of Learning Analytics},
	author = {Wasson, Barbara and Giannakos, Michail and Blikstad-Balas, Marte and Uppstad, Per Henning and Langford, Malcom and Bøhn, Einar Duenger},
	month = jul,
	year = {2024},
	keywords = {Promotion:Literaturanalyse, Charité:Promotion, Leraning:Analytics},
	pages = {1--13},
	file = {Wasson et al. - 2024 - Implementing Learning Analytics in Norway Four Ce.pdf:/Users/jochenhanisch-johannsen/Zotero/storage/28EK32XZ/Wasson et al. - 2024 - Implementing Learning Analytics in Norway Four Ce.pdf:application/pdf},
}

@article{chejara_impact_2024,
	title = {The {Impact} of {Attribute} {Noise} on the {Automated} {Estimation} of {Collaboration} {Quality} {Using} {Multimodal} {Learning} {Analytics} in {Authentic} {Classrooms}},
	copyright = {http://creativecommons.org/licenses/by-nc-nd/4.0},
	issn = {1929-7750},
	url = {https://learning-analytics.info/index.php/JLA/article/view/8253},
	doi = {10.18608/jla.2024.8253},
	abstract = {Multimodal learning analytics (MMLA) research has shown the feasibility of building automated models of collaboration quality using artificial intelligence (AI) techniques (e.g., supervised machine learning (ML)), thus enabling the development of monitoring and guiding tools for computer-supported collaborative learning (CSCL). However, the practical applicability and performance of these automated models in authentic settings remains largely an under-researched area. In such settings, the quality of data features or attributes is often affected by noise, which is referred to as attribute noise. This paper undertakes a systematic exploration of the impact of attribute noise on the performance of different collaboration-quality estimation models. Moreover, we also perform a comparative analysis of different ML algorithms in terms of their capability of dealing with attribute noise. We employ four ML algorithms that have often been used for collaboration-quality estimation tasks due to their high performance: random forest, naive Bayes, decision tree, and AdaBoost. Our results show that random forest and decision tree outperformed other algorithms for collaboration-quality estimation tasks in the presence of attribute noise. The study contributes to the MMLA (and learning analytics (LA) in general) and CSCL fields by illustrating how attribute noise impacts collaboration-quality model performance and which ML algorithms seem to be more robust to noise and thus more likely to perform well in authentic settings. Our research outcomes offer guidance to fellow researchers and developers of (MM)LA systems employing AI techniques with multimodal data to model collaboration-related constructs in authentic classroom settings.},
	language = {en},
	urldate = {2024-08-02},
	journal = {Journal of Learning Analytics},
	author = {Chejara, Pankaj and Prieto, Luis P. and Dimitriadis, Yannis and Rodríguez-Triana, María Jesús and Ruiz-Calleja, Adolfo and Kasepalu, Reet and Shankar, Shashi Kant},
	month = jun,
	year = {2024},
	keywords = {Promotion:Literaturanalyse, Charité:Promotion, Leraning:Analytics},
	file = {Chejara et al. - 2024 - The Impact of Attribute Noise on the Automated Est.pdf:/Users/jochenhanisch-johannsen/Zotero/storage/UEJ2AZVF/Chejara et al. - 2024 - The Impact of Attribute Noise on the Automated Est.pdf:application/pdf},
}

@incollection{cheng_insights_2024,
	address = {Cham},
	title = {Insights into {Precision} {Education} {Through} {Multimodal} {Learning} {Analytics} in {STEM} {Education}},
	volume = {14785},
	isbn = {978-3-031-65880-8 978-3-031-65881-5},
	url = {https://link.springer.com/10.1007/978-3-031-65881-5_7},
	abstract = {This study aims to explore the application of precision education in STEM education through multimodal learning analytics systems to enhance students’ learning outcomes and knowledge construction behaviors. A quasiexperimental design was implemented, comprising a control group and two experimental groups, the latter of which utilized single-modal and multimodal learning analytics systems, respectively. The results demonstrated signiﬁcant improvements in learning outcomes for students in the experimental groups compared to the control group, particularly for those using the multimodal learning analytics system. Moreover, the multimodal system was more effective in capturing students’ learning behaviors and interactions, facilitating more effective teaching interventions and guidance. This study underscores the potential of multimodal learning analytics in precision education and its signiﬁcant impact on improving the quality and outcomes of STEM education.},
	language = {en},
	urldate = {2024-08-02},
	booktitle = {Innovative {Technologies} and {Learning}},
	publisher = {Springer Nature Switzerland},
	author = {Lin, Chia-Ju and Pedaste, Margus and Huang, Yueh-Min},
	editor = {Cheng, Yu-Ping and Pedaste, Margus and Bardone, Emanuele and Huang, Yueh-Min},
	year = {2024},
	doi = {10.1007/978-3-031-65881-5_7},
	note = {Series Title: Lecture Notes in Computer Science},
	keywords = {Promotion:Literaturanalyse, Charité:Promotion, Leraning:Analytics},
	pages = {57--63},
	file = {Lin et al. - 2024 - Insights into Precision Education Through Multimod.pdf:/Users/jochenhanisch-johannsen/Zotero/storage/VYIMIWAF/Lin et al. - 2024 - Insights into Precision Education Through Multimod.pdf:application/pdf},
}

@article{shegupta_learning_2024,
	title = {Learning {Analytics} {Driven} {ARC}-{Tutoring} for {Individual} {Study} {Success}},
	abstract = {Students have to face challenges in applying scientific research skills during their internship and thesis writing at the universities. For this purpose, they receive some static web information and in the best cases holistic mentoring support from supervisors. However, they often require additional assistance in getting suggestions, immediate responses to errors, scaffolding, and reminders of their own learning goals. In this doctoral study, the concept of ARC tutoring guided by learning analytics has been realized as a proposition to address the aforementioned need for assistance in study success in higher education. It advocates leveraging learning experience data by employing learning analytics to develop the assessment, recommendation, and conversational agent (ARC) integrated tutoring workbench featuring distinct learner and tutor perspectives. This will enable the student to gain access to performance metrics and semi-automated individualized tutoring support, while tutors can observe group and individual performance, facilitating required interventions.},
	language = {en},
	author = {Shegupta, Ummay Ubaida},
	month = may,
	year = {2024},
	keywords = {Promotion:Literaturanalyse, Charité:Promotion, Leraning:Analytics},
	file = {Shegupta - Learning Analytics Driven ARC-Tutoring for Individ.pdf:/Users/jochenhanisch-johannsen/Zotero/storage/3ZM2UYID/Shegupta - Learning Analytics Driven ARC-Tutoring for Individ.pdf:application/pdf},
}

@article{julian_privacy_2024,
	title = {Privacy of {Sequential} {Data} for {Learning} {Analytics}},
	abstract = {Sequential data from multimodal learning experience and data source could provide the risk of background knowledge and also be exposed to third parties who may use them for malicious purposes, such as identity theft. This problem has been treated for Learning Analytics researchers with a general focus prioritizing the removal of direct identifiers over collected data. Nonetheless, the issue of collecting too much data and source anonymization methods for collecting sequential data with the aim of limiting sequential information had not been addressed until now. This research addresses the issue of collecting sequential data in a scalable manner and with a trade-off between privacy, accuracy and utility using sketching methods and differential privacy.},
	language = {en},
	author = {Julián, Anailys Hernández and Rodríguez-García, Mercedes and Manuel, Juan},
	month = may,
	year = {2024},
	keywords = {Promotion:Literaturanalyse, Charité:Promotion, Leraning:Analytics},
	pages = {8},
	file = {Julián et al. - Privacy of Sequential Data for Learning Analytics.pdf:/Users/jochenhanisch-johannsen/Zotero/storage/C3Y96Z5Q/paper2.pdf:application/pdf},
}

@article{wong_utilisation_2024,
	title = {Utilisation of {Learning} {Analytics} to {Identify} {Students} at {Risk} of {Poor} {Academic} {Performance} in {Medical} {Schools}},
	issn = {2168-8184},
	url = {https://www.cureus.com/articles/280618-utilisation-of-learning-analytics-to-identify-students-at-risk-of-poor-academic-performance-in-medical-schools},
	doi = {10.7759/cureus.66278},
	abstract = {Methods A retrospective study was conducted on a group of 235 students from the University of Edinburgh Bachelor of Medicine and Surgery (MBChB) in Year One for eight weeks from the start of term, September 2020. Purposive sampling was used. Data were collected on total test submissions, total discussion board submissions, engagement scores, and overall exam scores. Learning analytics on discussion board engagement were collected for new medical students before they had sat any summative assessment. Tests completed, discussion board posts made, and their total engagement score were correlated with their first summative assessment scores at the end of semester one.
Results We found a statistically significant correlation between total test submissions, total discussion board submissions, engagement scores, and overall exam scores, with small-medium effects (r = 0.281, p{\textless}0.001) (r = 0.241, p{\textless}0.001), and (r = 0.202, p{\textless}0.001). Students with more test submissions, total discussion board submissions, and total engagement had a higher overall exam score. There was a statistically significant moderate correlation between total submissions and overall exam scores (r = 0.324, p{\textless}0.001).
Conclusions Students who had a higher number of submissions were more likely to perform better on assessments. Early engagement correlates with performance. Learning analytics can help identify student underperformance before they undertake any assessment, and this can be done very inexpensively and with minimal staff resources if properly planned.},
	language = {en},
	urldate = {2024-08-10},
	journal = {Cureus},
	author = {Wong, Thai Ling and Hope, David and Jaap, Alan},
	month = aug,
	year = {2024},
	keywords = {Promotion:Literaturanalyse, Charité:Promotion, Leraning:Analytics},
	file = {Wong et al. - 2024 - Utilisation of Learning Analytics to Identify Stud.pdf:/Users/jochenhanisch-johannsen/Zotero/storage/PH7D9C5U/Wong et al. - 2024 - Utilisation of Learning Analytics to Identify Stud.pdf:application/pdf},
}

@phdthesis{schulz_game_2024,
	title = {Game {Design} im {Kontext} von {Kunst} und {Kunstunterricht}: digitale {Spielentwicklung} aus kunstpädagogischer {Perspektive}},
	copyright = {Creative Commons Attribution 4.0 International},
	shorttitle = {Game {Design} im {Kontext} von {Kunst} und {Kunstunterricht}},
	url = {https://publishup.uni-potsdam.de/64177},
	abstract = {This thesis’ objective is to closely examine game design as a constructivist approach to digital gaming in school, amidst the growing relevance and popularity of games and gamification as teaching methods within a culture of digitality. More specifically, game design will be analyzed in regard to how it applies in teaching art. To do so, this paper will explore in how far this method supports learning in general, as well as how it contributes to establishing student’s digital literacy. For this, the focus will be on looking at game design and its role in developing the crucial dimensions of competences and learning in art education. Hence, artistic production and aesthetic reception as the significant artistic competences will serve as the levels of analysis. In addition, the aesthetic experience as a special instance of learning, which in art educational discourse accounts for the highest goal of teaching along with the mentioned competences, will also be considered in the research. Through this analysis, game design transpires as generally beneficial for all three mentioned aspects of art education. Although for the sensory perception, a part of the aesthetic perception’s process, it solely takes on a complementary function, where it generally works in favor of this area of competence. As for the artistic production, not all areas of creation are addressed directly as well as an experimental artistic work not being fully enabled. However, all other aspects of this competence are appealed to. Regarding the aesthetic experience game design appears as particularly beneficial as it facilitates it fully. Thus, the application of digital game design in teaching art in school may be justified from an art educational perspective. Moreover, taking STEAM Education and project-based learning into consideration, its use may even be considered as recommendable.},
	language = {de},
	urldate = {2024-08-19},
	author = {Schulz, Florian},
	year = {2024},
	note = {Artwork Size: 508 KB, 50 pages
Medium: application/pdf
Publisher: Universität Potsdam},
	keywords = {Promotion:Literaturanalyse, 700 Künste; Bildende und angewandte Kunst, Charité:Promotion},
	file = {Schulz - 2024 - Game Design im Kontext von Kunst und Kunstunterric.pdf:/Users/jochenhanisch-johannsen/Zotero/storage/X4AA28IL/Schulz - 2024 - Game Design im Kontext von Kunst und Kunstunterric.pdf:application/pdf},
}

@article{solano_work_2024,
	title = {Work {Route} for the {Inclusion} of {Learning} {Analytics} in the {Development} of {Interactive} {Multimedia} {Experiences} for {Elementary} {Education}},
	volume = {14},
	copyright = {https://creativecommons.org/licenses/by/4.0/},
	issn = {2076-3417},
	url = {https://www.mdpi.com/2076-3417/14/17/7645},
	doi = {10.3390/app14177645},
	abstract = {Interactive multimedia experiences (IME) can be a pedagogical resource that has a strong potential to enhance learning experiences in early childhood. Learning analytics (LA) has become an important tool that allows us to understand more clearly how these multimedia experiences can contribute to the learning processes of these students. This article proposes a work route that deﬁnes a set of activities and techniques, as well as a ﬂow for their application, by taking into consideration the importance of including LA guidelines when designing IMEs for elementary education. The work route’s graphical representation is inspired by the foundations of the Essence standard’s graphical notation language. The guidelines are grouped into ﬁve categories, namely (i) a data analytics dashboard, (ii) student data, (iii) teacher data, (iv) learning activity data, and (v) student progress data. The guidelines were validated through two approaches. The ﬁrst involved a case study, where the guidelines were applied to an IME called Coco Shapes, which was aimed at transition students at the Colegio La Fontaine in Cali (Colombia), and the second involved the judgments of experts who examined the usefulness and clarity of the guidelines. The results from these approaches allowed us to obtain precise and effective feedback regarding the hypothesis under study. Our ﬁndings provide promising evidence of the value of our guidelines, which were included in the design of an IME and contributed to the greater personalized monitoring available to teachers to evaluate student learning.},
	language = {en},
	number = {17},
	urldate = {2024-09-04},
	journal = {Applied Sciences},
	author = {Solano, Andrés and Peláez, Carlos Alberto and Ospina, Johann A. and Luna-García, Huizilopoztli and Parra, Jorge Andrick and Ramírez, Gabriel Mauricio and Moreira, Fernando and López Sotelo, Jesús Alfonso and Villalba-Condori, Klinge Orlando},
	month = aug,
	year = {2024},
	keywords = {Promotion:Literaturanalyse, Charité:Promotion, Leraning:Analytics},
	pages = {7645},
	file = {Solano et al. - 2024 - Work Route for the Inclusion of Learning Analytics.pdf:/Users/jochenhanisch-johannsen/Zotero/storage/YAYLADHW/Solano et al. - 2024 - Work Route for the Inclusion of Learning Analytics.pdf:application/pdf},
}

@article{lobo_quintero_studying_2024,
	title = {Studying the {Flow} {Experience} in {Computer}-{Supported} {Collaborative} {Learning}: {A} {Study} with {PyramidApp}},
	copyright = {http://creativecommons.org/licenses/by-nc-nd/3.0},
	issn = {1929-7750},
	shorttitle = {Studying the {Flow} {Experience} in {Computer}-{Supported} {Collaborative} {Learning}},
	url = {https://www.learning-analytics.info/index.php/JLA/article/view/8185},
	doi = {10.18608/jla.2024.8185},
	abstract = {Computer-Supported Collaborative Learning (CSCL) is recognized as an effective methodology for fostering social interaction mediated by technology in ways that potentially trigger learning. The successful implementation of CSCL hinges on factors such as the scripting mechanics for activity sequencing proposed by Collaborative Learning Flow Patterns (CLFP). Yet, research in CSCL scripts has not studied if CLFPs achieves the so-called notion of “flow experience,” defined as an optimal state in which individuals are engaged and absorbed in an activity. This study proposes an approach to measure flow in the case of the Pyramid CLFP and studies the factors that influence the flow experience in the PyramidApp tool. The study tests a model that uses analysis of the Flow Short Scale and data logs. The findings show that there is a relationship between factors such as the speed of individual contributions and active participation in groups with the flow experience. Notably, the quantity of participation does not exhibit a discernible impact on the flow. The study emphasizes the interest of the modelled factors and the proposed approach for learning analytics to understand the flow experience in CLFP implementations.},
	language = {en},
	urldate = {2024-10-20},
	journal = {Journal of Learning Analytics},
	author = {Lobo Quintero, René Alejandro and Sánchez-Reina, Roberto and Hernández-Leo, Davinia},
	month = oct,
	year = {2024},
	keywords = {Promotion:Literaturanalyse, Charité:Promotion, Leraning:Analytics},
	pages = {1--17},
	file = {Lobo Quintero et al. - 2024 - Studying the Flow Experience in Computer-Supported.pdf:/Users/jochenhanisch-johannsen/Zotero/storage/AY6CQYBD/Lobo Quintero et al. - 2024 - Studying the Flow Experience in Computer-Supported.pdf:application/pdf},
}

@article{zine_implementing_2024,
	title = {Implementing learning analytics in higher education: {A} case study on challenges and requirements},
	volume = {8},
	issn = {2576-8484},
	shorttitle = {Implementing learning analytics in higher education},
	url = {https://learning-gate.com/index.php/2576-8484/article/view/2384},
	doi = {10.55214/25768484.v8i6.2384},
	abstract = {Learning analytics (LA) offers an effective solution for leveraging big data to increase retention rates, reduce quality gaps, improve resource allocation, monitor skills development, and raise graduation rates. To evaluate the potential benefits of this solution, this research explores learning analytics in Moroccan higher education, focusing on the implementation needs and challenges. The methodology is based on qualitative research conducted through 10 interviews with teachers from a multidisciplinary faculty in Nador. The findings of this research reveal that few professors use learning analytics tools on their own initiative. They express a need for guidance, support, and user-friendly tools. Most respondents also mentioned various challenges to adopting learning analytics, such as the inability to align learning analytics with teaching practices, ethical and confidentiality concerns, and limitations in data value, all of which can hinder the effectiveness of learning analytics. Additional challenges include a heavy workload, lack of necessary resources, insufficient technological and pedagogical skills, and difficulties in operating learning analytics tools. Furthermore, managing unorganized data and addressing the heterogeneity of databases and datasets present significant obstacles. Lastly, inadequate preparation on the part of faculty members can impede the full realization of the advantages of learning analytics. To address these challenges, several recommendations have been formulated, including the need for training, guidance, the development of policies, models, and frameworks for the adoption of learning analytics, and the certification of learning analytics tools that are useful and easy to use.},
	language = {en},
	number = {6},
	urldate = {2024-10-22},
	journal = {Edelweiss Applied Science and Technology},
	author = {Zine, Abdelkhalek and Kaaouachi, Abdelali},
	month = oct,
	year = {2024},
	keywords = {Promotion:Literaturanalyse, Charité:Promotion, Leraning:Analytics},
	pages = {2048--2055},
	file = {Zine und Kaaouachi - 2024 - Implementing learning analytics in higher educatio.pdf:/Users/jochenhanisch-johannsen/Zotero/storage/WZDCUIHW/Zine und Kaaouachi - 2024 - Implementing learning analytics in higher educatio.pdf:application/pdf},
}

@techreport{mmb_institut_goldgraberstimmung_2024,
	address = {Essen},
	type = {Trendstudie},
	title = {Goldgräberstimmung durch {GenAI} {KI} beflügelt die {Bildungsbranche}: {Ergebnisse} der 18. {Trendstudie} mmb {Learning} {Delphi}},
	shorttitle = {mmb-{Trendmonitor} 2023/2024},
	url = {https://de.statista.com/statistik/daten/studie/203748/umfrage/bedeutung-von-e-learning-anwendungen-in-unternehmen/},
	language = {de-DE},
	urldate = {2024-10-22},
	institution = {MMB-Institut für Medien- und Kompetenzforschung GmbH},
	author = {{mmb Institut}},
	year = {2024},
	keywords = {Promotion:Literaturanalyse, Charité:Promotion, Promotion:Literaturanalyse:Berichte},
	pages = {24},
	file = {mmb Institut - 2024 - Goldgräberstimmung durch GenAI KI beflügelt die Bi.pdf:/Users/jochenhanisch-johannsen/Zotero/storage/34ZQAP9T/mmb Institut - 2024 - Goldgräberstimmung durch GenAI KI beflügelt die Bi.pdf:application/pdf},
}

@techreport{noauthor_d21-digital-index_nodate,
	title = {D21-{Digital}-{Index} 2023/2024 – {Jährliches} {Lagebild} zur {Digitalen} {Gesellschaft}},
	language = {de},
	keywords = {Promotion:Literaturanalyse, Charité:Promotion, Promotion:Literaturanalyse:Berichte},
	file = {D21-Digital-Index 20232024 – Jährliches Lagebild .pdf:/Users/jochenhanisch-johannsen/Zotero/storage/KX6424KT/D21-Digital-Index 20232024 – Jährliches Lagebild .pdf:application/pdf},
}

@techreport{noauthor_d21-studie_digitales_lebenpdf_nodate,
	title = {d21-studie\_digitales\_leben.pdf},
	keywords = {Promotion:Literaturanalyse, Charité:Promotion, Promotion:Literaturanalyse:Berichte},
	file = {d21-studie_digitales_leben.pdf:/Users/jochenhanisch-johannsen/Zotero/storage/YLYRPCHZ/d21-studie_digitales_leben.pdf:application/pdf},
}

@techreport{noauthor_digital-skills-gap_so-unterschiedlich-digital-kompetent-ist--deutsche-bevoelkerungpdf_nodate,
	title = {digital-skills-gap\_so-unterschiedlich-digital-kompetent-ist-die-deutsche-bevoelkerung.pdf},
	keywords = {Promotion:Literaturanalyse, Charité:Promotion, Promotion:Literaturanalyse:Berichte},
	file = {digital-skills-gap_so-unterschiedlich-digital-kompetent-ist-die-deutsche-bevoelkerung.pdf:/Users/jochenhanisch-johannsen/Zotero/storage/GTFJ5KS2/digital-skills-gap_so-unterschiedlich-digital-kompetent-ist-die-deutsche-bevoelkerung.pdf:application/pdf},
}

@techreport{initiative_d21_e_v_21st_nodate,
	type = {Lagebild},
	title = {21st {Century} {Schools}: {Lagebild} des digitalen {Schulunterrichts} in den 16 {Bundesländern} aus {Sicht} der {Eltern}},
	editor = {{Initiative D21 e. V.}},
	keywords = {Promotion:Literaturanalyse, Charité:Promotion, Promotion:Literaturanalyse:Berichte},
	file = {21stcenturyschools.pdf:/Users/jochenhanisch-johannsen/Zotero/storage/NKPZ6IUL/21stcenturyschools.pdf:application/pdf},
}

@techreport{siepmann_lern-okosysteme_2022,
	address = {Hagen im Bremischen},
	type = {Teilstudie},
	title = {Lern-Ökosysteme und {Bildungstechnologie}},
	url = {https://www.elearning-journal.com/ebook-bms2022-lernoekosysteme/},
	language = {de-DE},
	urldate = {2024-10-22},
	institution = {Siepmann Media, Redaktion eLearning Journal},
	editor = {Siepmann, Frank},
	year = {2022},
	keywords = {Promotion:Argumentation, Promotion:Literaturanalyse, Charité:Promotion, Promotion:Literaturanalyse:Berichte},
	pages = {10},
	file = {eLJ_BMS2022_Lernoekosystem.pdf:/Users/jochenhanisch-johannsen/Zotero/storage/6J9JTMAP/eLJ_BMS2022_Lernoekosystem.pdf:application/pdf},
}

@incollection{lang_emotional_2022,
	edition = {2},
	title = {Emotional {Learning} {Analytics}},
	isbn = {978-0-9952408-3-4},
	url = {https://www.solaresearch.org/publications/hla-22/hla22-chapter12/},
	abstract = {This chapter discusses the ubiquity and importance of emotion to learning. It argues substantial progress can be made by coupling discovery-oriented, data-driven, analytic methods of learning analytics and educational data mining with theoretical advances and methodologies from the affective and learning sciences. Core, emerging, and future themes of research at the intersection of these areas are discussed.},
	language = {en},
	urldate = {2024-10-26},
	booktitle = {The {Handbook} of {Learning} {Analytics}},
	publisher = {SOLAR},
	author = {D’Mello, Sidney K. and Jensen, Emily},
	editor = {Lang, Charles and Siemens, George and Wise, Alyssa Friend},
	collaborator = {Gašević, Dragan and Merceron, Agathe},
	year = {2022},
	doi = {10.18608/hla22.012},
	keywords = {Promotion:Literaturanalyse, Charité:Promotion, Leraning:Analytics},
	pages = {120--129},
	file = {D’Mello und Jensen - 2022 - Emotional Learning Analytics.pdf:/Users/jochenhanisch-johannsen/Zotero/storage/D4FCXLQ4/D’Mello und Jensen - 2022 - Emotional Learning Analytics.pdf:application/pdf},
}

@article{gorzen_evaluation_nodate,
	title = {Evaluation of a framework for the integrating of {Learning} {Analytics} in {Virtual} {Reality}},
	abstract = {The sustainable integration of Learning Analytics (complying with the FAIR principles) in an eXtended Reality application is complex. Following an iterative process, a framework and ecosystem were developed to overcome this and provide guidance for producing research software with reproducibility, reusability, and accuracy in mind. Using the solution, the requirements for new projects have been seamlessly expanded. We observed seven Computer Science students while integrating Learning Analytics into a Virtual Reality application to explore the current state of the framework and ecosystem regarding productivity, workflow, usability, functionality, and challenges. This paper presents and reflects the method and results of our observation study.},
	language = {en},
	author = {Görzen, Sergej and Heinemann, Birte and Schroeder, Ulrik},
	keywords = {Promotion:Literaturanalyse, Charité:Promotion, Leraning:Analytics},
	file = {PDF:/Users/jochenhanisch-johannsen/Zotero/storage/TKCM9U5Y/Görzen et al. - Evaluation of a framework for the integrating of Learning Analytics in Virtual Reality.pdf:application/pdf},
}

@misc{tirado_towards_2024,
	title = {Towards an {Operational} {Responsible} {AI} {Framework} for {Learning} {Analytics} in {Higher} {Education}},
	url = {http://arxiv.org/abs/2410.05827},
	abstract = {Universities are increasingly adopting data-driven strategies to enhance student success, with AI applications like Learning Analytics (LA) and Predictive Learning Analytics (PLA) playing a key role in identifying at-risk students, personalising learning, supporting teachers, and guiding educational decision-making. However, concerns are rising about potential harms these systems may pose, such as algorithmic biases leading to unequal support for minority students. While many have explored the need for Responsible AI in LA, existing works often lack practical guidance for how institutions can operationalise these principles. In this paper, we propose a novel Responsible AI framework tailored specifically to LA in Higher Education (HE). We started by mapping 11 established Responsible AI frameworks, including those by leading tech companies, to the context of LA in HE. This led to the identification of seven key principles such as transparency, fairness, and accountability. We then conducted a systematic review of the literature to understand how these principles have been applied in practice. Drawing from these findings, we present a novel framework that offers practical guidance to HE institutions and is designed to evolve with community input, ensuring its relevance as LA systems continue to develop.},
	language = {en},
	urldate = {2024-11-08},
	publisher = {arXiv},
	author = {Tirado, Alba Morales and Mulholland, Paul and Fernandez, Miriam},
	month = oct,
	year = {2024},
	note = {arXiv:2410.05827 [cs]},
	keywords = {Computer Science - Computers and Society, Computer Science - Artificial Intelligence, Promotion:Literaturanalyse, Charité:Promotion, Leraning:Analytics},
	file = {PDF:/Users/jochenhanisch-johannsen/Zotero/storage/89PQAGH9/Tirado et al. - 2024 - Towards an Operational Responsible AI Framework for Learning Analytics in Higher Education.pdf:application/pdf},
}

@incollection{longmus_worauf_2021,
	address = {Berlin, Heidelberg},
	title = {Worauf kommt es an? {Qualitätssicherung} im agilen {Lernen}},
	isbn = {978-3-662-62012-0 978-3-662-62013-7},
	shorttitle = {Worauf kommt es an?},
	url = {http://link.springer.com/10.1007/978-3-662-62013-7_12},
	language = {de},
	urldate = {2024-11-08},
	booktitle = {Agiles {Lernen} im {Unternehmen}},
	publisher = {Springer Berlin Heidelberg},
	author = {Jungclaus, Joana},
	editor = {Longmuß, Jörg and Korge, Gabriele and Bauer, Agnes and Höhne, Benjamin},
	year = {2021},
	doi = {10.1007/978-3-662-62013-7_12},
	keywords = {FernUni-Hagen:MABM:Master-Arbeit, Promotion:Literaturanalyse, Charité:Promotion},
	pages = {111--118},
	file = {PDF:/Users/jochenhanisch-johannsen/Zotero/storage/T9BUZMRH/Jungclaus - 2021 - Worauf kommt es an Qualitätssicherung im agilen Lernen.pdf:application/pdf},
}

@incollection{plass_game-based_2025,
	address = {Cham},
	title = {Game-{Based} {Learning} {Analytics}: {Insights} from an {Integrated} {Design} {Process}},
	volume = {15259},
	isbn = {978-3-031-74137-1 978-3-031-74138-8},
	shorttitle = {Game-{Based} {Learning} {Analytics}},
	url = {https://link.springer.com/10.1007/978-3-031-74138-8_9},
	abstract = {Game-Based Learning Analytics (GBLA) is a method of integrating Game-Based Learning and Learning Analytics to enhance the eﬀectiveness of the learning process in educational games by providing actionable learning analytics information to players within the game environment. This paper presents initial insights from an integrated design process to achieve this goal. Through a series of interdisciplinary workshops culminating in a participant playtest session, this paper highlights the challenges and opportunities that arise from this integration. The ﬁndings point to the importance of early consideration of learning analytics in game design, the challenges of conceptualizing the diﬀerences between game feedback and learning feedback, and how learners interpret learning feedback within the context of the game. This work lays the groundwork for future research and development in the interaction between Game-Based Learning and Learning Analytics.},
	language = {en},
	urldate = {2024-11-12},
	booktitle = {Serious {Games}},
	publisher = {Springer Nature Switzerland},
	author = {Boothe, Maurice and Gopalakrishnan, Madhumitha and Huynh, Mischa and Wang, Yanzhi and Ochoa, Xavier},
	editor = {Plass, Jan L. and Ochoa, Xavier},
	year = {2025},
	doi = {10.1007/978-3-031-74138-8_9},
	note = {Series Title: Lecture Notes in Computer Science},
	keywords = {Promotion:Literaturanalyse, Charité:Promotion, Leraning:Analytics},
	pages = {108--123},
	file = {PDF:/Users/jochenhanisch-johannsen/Zotero/storage/8MAVGNUF/Boothe et al. - 2025 - Game-Based Learning Analytics Insights from an Integrated Design Process.pdf:application/pdf},
}

@article{clark_tailoring_2024,
	title = {Tailoring {Learning} {Analytics} for {Success}: {Insights} from a {Comparative} {Study} of {Australian} {Universities}},
	abstract = {Learning Analytics systems are emerging as a powerful tool for student success, optimising curricula, and informing data-driven decisions. However, these will not be achieved without effective implementation strategies tailored to institutional contexts. This study compares the learning analytics practices of five diverse Australian universities, offering a nuanced exploration of the similarities, differences, and patterns that characterise their adoption journeys. We will work to identify the factors that contribute to successful implementation and enhanced impact. Our findings emphasise the importance of aligning learning analytics initiatives with institutional contexts, student demographics, and unique needs, emphasising the necessity of tailored approaches that resonate with stakeholders and address specific challenges. Our intention is to provide a practical roadmap for Higher Education Institutions seeking to benefit from learning analytics, giving them the means to harness the full potential of these tools.},
	language = {en},
	author = {Clark, Jo-Anne and Tuffley, David},
	year = {2024},
	keywords = {Promotion:Literaturanalyse, Charité:Promotion, Leraning:Analytics},
	file = {PDF:/Users/jochenhanisch-johannsen/Zotero/storage/B4JJJ24Y/Clark und Tuffley - 2024 - Tailoring Learning Analytics for Success Insights from a Comparative Study of Australian Universiti.pdf:application/pdf},
}

@article{ruhland_auf_nodate,
	title = {Auf dem {Weg} zu {Learning} {Analytics} in der {Praxis}},
	language = {de},
	author = {Ruhland, Claudia},
	keywords = {Promotion:Literaturanalyse, Charité:Promotion, Leraning:Analytics},
	file = {PDF:/Users/jochenhanisch-johannsen/Zotero/storage/KBG27HTG/Ruhland - Auf dem Weg zu Learning Analytics in der Praxis.pdf:application/pdf},
}

@article{teng_systematic_nodate,
	title = {Systematic {Review} on the {Application} of {Multimodal} {Learning} {Analytics} to {Personalize} {Students}’ {Learning}},
	abstract = {In personalized learning (PL), learning processes are customized to account for student skills and preferences. However, as PL is generally based on a single data type, it cannot wholly represent students’ learning behaviors and progress. Hence, it is crucial to leverage Multimodal Learning Analytics (MMLA) in PL to alleviate these restrictions. A systematic literature review was conducted to explore the use of MMLA in PL and investigate its benefits across several contexts and approaches. The underexplored aspects of MMLA in PL, like the gaps in topics, pedagogies, learning settings and environments, populations, and modalities studied, are addressed, and MMLA’s potential to provide real-time tailored feedback and improve engagement is discussed.},
	language = {en},
	author = {Teng, Khor Ean and Ping, Tan Le and Leta, Chan Shi Hui},
	keywords = {Promotion:Literaturanalyse, Charité:Promotion, Leraning:Analytics},
	file = {PDF:/Users/jochenhanisch-johannsen/Zotero/storage/D82S52AB/Teng et al. - Systematic Review on the Application of Multimodal Learning Analytics to Personalize Students’ Learn.pdf:application/pdf},
}

@incollection{bartimote_towards_2024,
	address = {Cham},
	title = {Towards a {Genealogical} {Critical} {Theory} of {Learning} {Analytics}},
	isbn = {978-3-031-60570-3 978-3-031-60571-0},
	url = {https://link.springer.com/10.1007/978-3-031-60571-0_13},
	abstract = {This chapter makes a distinction between two approaches to theory in Learning Analytics (LA). The first approach pursues the identification of suitable theories ‘for’ LA and requires a commitment to the scientific study of learning. The second approach rests upon a broader sociological outlook on Learning Analytics as a field of knowledge. The chapter is concerned with the second approach and proposes a sociological theory of the ‘LA Gaze’. It examines the underlying principles of a theory of the LA Gaze and explores, using a genealogical approach informed by the sociology of scientific knowledge, the social, historical, and material relations between Learning Analytics and contiguous epistemic fields: computer and data science, Educational Data Mining (EDM) and educational research. The chapter contributes to the LA field by encouraging a historical and critical reflection on its origins and its future directions.},
	language = {en},
	urldate = {2025-01-01},
	booktitle = {Theory {Informing} and {Arising} from {Learning} {Analytics}},
	publisher = {Springer Nature Switzerland},
	author = {Perrotta, Carlo},
	editor = {Bartimote, Kathryn and Howard, Sarah K. and Gašević, Dragan},
	year = {2024},
	doi = {10.1007/978-3-031-60571-0_13},
	keywords = {Promotion:Literaturanalyse, Charité:Promotion, Leraning:Analytics},
	pages = {203--217},
	file = {PDF:/Users/jochenhanisch-johannsen/Zotero/storage/PFTDJQ6E/Perrotta - 2024 - Towards a Genealogical Critical Theory of Learning Analytics.pdf:application/pdf},
}

@incollection{bartimote_theories_2024,
	address = {Cham},
	title = {Theories {All} the {Way} {Across}: {The} {Role} of {Theory} in {Learning} {Analytics} and the {Case} for {Unified} {Methods}},
	isbn = {978-3-031-60570-3 978-3-031-60571-0},
	shorttitle = {Theories {All} the {Way} {Across}},
	url = {https://link.springer.com/10.1007/978-3-031-60571-0_12},
	abstract = {We argue that learning analytics research—and really any empirical research—is composed of a primary modeling pathway: a series of steps that progressively reduce the extraneous information that confronts a researcher and thus clarifies important claims about, issues in, and properties of some activity in the world. Each of these steps is necessarily informed by some theoretical perspective (implicit or explicit) and is therefore contestable. Because none of these analytical steps can be validated from within, research is stronger when qualitative and quantitative methods are unified in the research process.},
	language = {en},
	urldate = {2025-01-01},
	booktitle = {Theory {Informing} and {Arising} from {Learning} {Analytics}},
	publisher = {Springer Nature Switzerland},
	author = {Shaffer, David Williamson and Ruis, A. R.},
	editor = {Bartimote, Kathryn and Howard, Sarah K. and Gašević, Dragan},
	year = {2024},
	doi = {10.1007/978-3-031-60571-0_12},
	keywords = {Promotion:Literaturanalyse, Charité:Promotion, Leraning:Analytics},
	pages = {187--201},
	file = {PDF:/Users/jochenhanisch-johannsen/Zotero/storage/8EPJK9Q5/Shaffer und Ruis - 2024 - Theories All the Way Across The Role of Theory in Learning Analytics and the Case for Unified Metho.pdf:application/pdf},
}

@incollection{bartimote_what_2024,
	address = {Cham},
	title = {What {Could} {Learning} {Analytics} {Learn} from {Human}-{Computer} {Interaction} {Theory}?},
	isbn = {978-3-031-60570-3 978-3-031-60571-0},
	url = {https://link.springer.com/10.1007/978-3-031-60571-0_10},
	abstract = {The design of Learning Analytics tools is an example of the general problem of designing interactive tools, which is the focus of Human-Computer Interaction (HCI) research and design practice. LA as a field must understand how to embed LA into organisations and the design of effective, trustworthy humancomputer systems is where HCI theory and practice have much to offer. Consequently, this chapter argues that LA can learn from (i) the way that theory has evolved in HCI, (ii) the field’s methods for evaluating interactive systems at different scales, and (iii) HCI debates around how established scientific theories and methods relate to design theories and methods. As a highly interdisciplinary applied field, LA (like HCI) faces the challenge of maintaining academic standards in the conduct and review of research from many disciplinary traditions. I propose that HCI offers inspiration for researchers seeking rigorous methods to design and evaluate LA in authentic contexts, including principles to maintain their intellectual rigour, which will also be of interest to LA journals and conferences seeking to maintain peer review standards.},
	language = {en},
	urldate = {2025-01-01},
	booktitle = {Theory {Informing} and {Arising} from {Learning} {Analytics}},
	publisher = {Springer Nature Switzerland},
	author = {Shum, Simon Buckingham},
	editor = {Bartimote, Kathryn and Howard, Sarah K. and Gašević, Dragan},
	year = {2024},
	doi = {10.1007/978-3-031-60571-0_10},
	keywords = {Promotion:Literaturanalyse, Charité:Promotion, Leraning:Analytics},
	pages = {153--172},
	file = {PDF:/Users/jochenhanisch-johannsen/Zotero/storage/QKVXGVG8/Shum - 2024 - What Could Learning Analytics Learn from Human-Computer Interaction Theory.pdf:application/pdf},
}

@incollection{bartimote_emotion_2024,
	address = {Cham},
	title = {Emotion {Theory} and {Learning} {Analytics}: {A} {Theoretical} {Framework} for {Capturing} {Emotion} {Regulation} {Using} {Process} {Data}},
	isbn = {978-3-031-60570-3 978-3-031-60571-0},
	shorttitle = {Emotion {Theory} and {Learning} {Analytics}},
	url = {https://link.springer.com/10.1007/978-3-031-60571-0_8},
	abstract = {Emotion regulation (ER) refers to the constant monitoring and modulating one’s dysfunctional emotion states and is essential for increasing learning outcomes with advanced learning technologies. Prior studies have typically captured ER using static instruments (e.g., self-reports) while little has focused on using various types of process data, including facial expressions of emotions, log files, screen recordings, physiology, verbalizations, and eye tracking. This possibly stems from the general lack of guidance for researchers on how to capture ER processes as individuals learn with advanced learning technologies. With an enormous increase in the access to process data for researchers, this chapter contributes to the field of learning analytics by reflecting on state-of-the-art methods and describing a theoretically-driven framework to study each phase of ER using process data. Specifically, this chapter extends McRae and Gross’ (2020) ER model by identifying modalities of data that represent different phases of ER. This framework lays the theoretical groundwork for how future studies should incorporate multimodal data to capture the ER process, increasing the accessibility and utility of leveraging process data to a range of stakeholders for capturing and identifying ER using learning analytics.},
	language = {en},
	urldate = {2025-01-01},
	booktitle = {Theory {Informing} and {Arising} from {Learning} {Analytics}},
	publisher = {Springer Nature Switzerland},
	author = {Dever, Daryn A. and Cloude, Elizabeth B. and Wiedbusch, Megan D. and Azevedo, Roger},
	editor = {Bartimote, Kathryn and Howard, Sarah K. and Gašević, Dragan},
	year = {2024},
	doi = {10.1007/978-3-031-60571-0_8},
	keywords = {Promotion:Literaturanalyse, Charité:Promotion, Leraning:Analytics, Promotion:01-02},
	pages = {125--137},
	file = {PDF:/Users/jochenhanisch-johannsen/Zotero/storage/5DRE4YBU/Dever et al. - 2024 - Emotion Theory and Learning Analytics A Theoretical Framework for Capturing Emotion Regulation Usin.pdf:application/pdf},
}

@incollection{bartimote_theory_2024,
	address = {Cham},
	title = {Theory and {Intermediate}-{Level} {Knowledge} in {Multimodal} {Learning} {Analytics}},
	isbn = {978-3-031-60570-3 978-3-031-60571-0},
	url = {https://link.springer.com/10.1007/978-3-031-60571-0_6},
	abstract = {Advances in sensing technologies and computational analyses have demonstrated the potential to help us understand learning processes which were either not-possible to be captured or “too complex” for traditional analytics (e.g., eyetracking, temperature sensing, hear-rate sensing). This gave rise to the research space of Multimodal Learning Analytics (MMLA), which maintains Learning Analytics’ overarching goal of understanding and improving learning in all the different environments where it occurs, but also leverages advances in sensor technologies and computational analyses (e.g., sensor data processing, fusion, and analysis techniques). In this chapter, we introduce the reader to the field of MMLA and provide an overview of contemporary MMLA research. Moreover, we give an overview of theories used in MMLA and discuss the potential of MMLA to support the development of intermediate-level knowledge and how this knowledge, can be generated. To exemplify the importance and potential of intermediate-level knowledge in MMLA, we provide three examples on how knowledge that is more generative than an instantiation and yet not at the scope of generalized theory, manages to greatly advance MMLA research and practice.},
	language = {en},
	urldate = {2025-01-01},
	booktitle = {Theory {Informing} and {Arising} from {Learning} {Analytics}},
	publisher = {Springer Nature Switzerland},
	author = {Giannakos, Michail},
	editor = {Bartimote, Kathryn and Howard, Sarah K. and Gašević, Dragan},
	year = {2024},
	doi = {10.1007/978-3-031-60571-0_6},
	keywords = {Promotion:Literaturanalyse, Charité:Promotion, Leraning:Analytics, Promotion:01-02},
	pages = {87--104},
	file = {PDF:/Users/jochenhanisch-johannsen/Zotero/storage/2SZ9HLSF/Giannakos - 2024 - Theory and Intermediate-Level Knowledge in Multimodal Learning Analytics.pdf:application/pdf},
}

@incollection{bartimote_learning_2024,
	address = {Cham},
	title = {Learning {Analytics} {Framework} for {Analysing} {Regulation} in {Collaborative} {Learning} ({FARCL})},
	isbn = {978-3-031-60570-3 978-3-031-60571-0},
	url = {https://link.springer.com/10.1007/978-3-031-60571-0_5},
	abstract = {Learning regulation research has advanced via the conceptualisation of socially shared regulation (SSRL) in collaborative learning. However, empirical evidencing of SSRL has still faced several challenges due to the unobservability of the cognitive and emotional processes at the core of regulation. Fortunately, with the aid of learning analytics and cutting-edge technologies for collecting and processing immense data from multiple modalities and channels, we are on the edge of revealing those “invisible” metacognitive level processes and also progressing in developing metrics for measuring core processes of regulation. Nevertheless, a systematic understanding of how theories on regulation in learning can inform learning analytics to maximise its potential is still lacking. Accordingly, the aim of this chapter is to discuss and demonstrate through examples how the socially shared regulation theoretical framework can be applied to the design of learning analytics. Hence, we propose a generic methodological Framework for Analysing Regulation in Collaborative Learning (FARCL) for the purpose of establishing a foundation for methodological advancement in measuring and examining regulation in collaborative learning environments. FARCL provides needed guidance for learning scientists and educational technology researchers to assess self-regulation, co-regulation, and socially shared regulation in collaborative learning.},
	language = {en},
	urldate = {2025-01-01},
	booktitle = {Theory {Informing} and {Arising} from {Learning} {Analytics}},
	publisher = {Springer Nature Switzerland},
	author = {Nguyen, Andy and Järvelä, Sanna},
	editor = {Bartimote, Kathryn and Howard, Sarah K. and Gašević, Dragan},
	year = {2024},
	doi = {10.1007/978-3-031-60571-0_5},
	keywords = {Promotion:Literaturanalyse, Charité:Promotion, Leraning:Analytics},
	pages = {71--85},
	file = {PDF:/Users/jochenhanisch-johannsen/Zotero/storage/WXS2XLHK/Nguyen und Järvelä - 2024 - Learning Analytics Framework for Analysing Regulation in Collaborative Learning (FARCL).pdf:application/pdf},
}

@incollection{bartimote_conversation_2024,
	address = {Cham},
	title = {In {Conversation}: {Bannert}, {Molenaar}, \& {Winne} – {Multiple} {Perspectives} on {Researching} and {Supporting} {Self}-{Regulated} {Learning} via {Analytics}},
	isbn = {978-3-031-60570-3 978-3-031-60571-0},
	shorttitle = {In {Conversation}},
	url = {https://link.springer.com/10.1007/978-3-031-60571-0_4},
	language = {en},
	urldate = {2025-01-01},
	booktitle = {Theory {Informing} and {Arising} from {Learning} {Analytics}},
	publisher = {Springer Nature Switzerland},
	author = {Raković, Mladen and Bannert, Maria and Molenaar, Inge and Winne, Philip H. and Gašević, Dragan},
	editor = {Bartimote, Kathryn and Howard, Sarah K. and Gašević, Dragan},
	year = {2024},
	doi = {10.1007/978-3-031-60571-0_4},
	keywords = {Promotion:Literaturanalyse, Charité:Promotion, Leraning:Analytics},
	pages = {57--69},
	file = {PDF:/Users/jochenhanisch-johannsen/Zotero/storage/BM4TI833/Raković et al. - 2024 - In Conversation Bannert, Molenaar, & Winne – Multiple Perspectives on Researching and Supporting Se.pdf:application/pdf},
}

@incollection{bartimote_conversation_2024-1,
	address = {Cham},
	title = {In {Conversation}: {Gulson}, {Anderson}, \& {Prinsloo} – {Examining} {Theoretical} {Approaches} and {Future} {Directions} for {Ethics} in {Learning} {Analytics}},
	isbn = {978-3-031-60570-3 978-3-031-60571-0},
	shorttitle = {In {Conversation}},
	url = {https://link.springer.com/10.1007/978-3-031-60571-0_3},
	abstract = {In this ‘in conversation’ chapter we surface the role of theory in research on ethics in the field of learning analytics. We begin by reporting the emergence of theory-based discussions of ethics in the learning analytics literature during the first 5 years of the life of the field; and then highlight prominent theories in the literature since 2016 when the number of publications per year with a focus on ethical learning analytics increased markedly. However, the substantive work of the chapter is to share a conversation amongst three expert panel members Paul Prinsloo, Kalervo Gulson, and Theresa Anderson. In it, they examined various sociological horizons for fresh ideas that might newly shape the field’s metaethical questioning around the morality of educational data use. Stemming from this, they considered the likely impact a variety of standpoints could have on the range of principles and values that inform our data practices. All in all, it is a conversation that queries whether or not the theory we have is adequate for the current times, and suggests how it could be. A podcast of the conversation is available at https://spotifyanchor-web.app.link/e/ r5psLaA3MMb.},
	language = {en},
	urldate = {2025-01-01},
	booktitle = {Theory {Informing} and {Arising} from {Learning} {Analytics}},
	publisher = {Springer Nature Switzerland},
	author = {Bartimote, Kathryn and Gulson, Kalervo N. and Anderson, Theresa D. and Prinsloo, Paul},
	editor = {Bartimote, Kathryn and Howard, Sarah K. and Gašević, Dragan},
	year = {2024},
	doi = {10.1007/978-3-031-60571-0_3},
	keywords = {Promotion:Literaturanalyse, Charité:Promotion, Leraning:Analytics},
	pages = {39--53},
	file = {PDF:/Users/jochenhanisch-johannsen/Zotero/storage/YAJLKR47/Bartimote et al. - 2024 - In Conversation Gulson, Anderson, & Prinsloo – Examining Theoretical Approaches and Future Directio.pdf:application/pdf},
}

@incollection{bartimote_making_2024,
	address = {Cham},
	title = {Making {Bigger} {Waves}: {Automating} {Theoretical} {Coding} to {Generate} {Educationally} {Meaningful} {Learning} {Analytics}},
	isbn = {978-3-031-60570-3 978-3-031-60571-0},
	shorttitle = {Making {Bigger} {Waves}},
	url = {https://link.springer.com/10.1007/978-3-031-60571-0_2},
	language = {en},
	urldate = {2025-01-01},
	booktitle = {Theory {Informing} and {Arising} from {Learning} {Analytics}},
	publisher = {Springer Nature Switzerland},
	author = {Maton, Karl and Doran, Y. J. and Howard, Sarah K. and Nothman, Joel and Quilpatay, Mauricio},
	editor = {Bartimote, Kathryn and Howard, Sarah K. and Gašević, Dragan},
	year = {2024},
	doi = {10.1007/978-3-031-60571-0_2},
	keywords = {Promotion:Literaturanalyse, Charité:Promotion, Leraning:Analytics},
	pages = {19--37},
	file = {PDF:/Users/jochenhanisch-johannsen/Zotero/storage/I5L6YXBR/Maton et al. - 2024 - Making Bigger Waves Automating Theoretical Coding to Generate Educationally Meaningful Learning Ana.pdf:application/pdf},
}

@incollection{bartimote_theory_2024-1,
	address = {Cham},
	title = {Theory and {Learning} {Analytics}, a {Historical} {Perspective}},
	isbn = {978-3-031-60570-3 978-3-031-60571-0},
	url = {https://link.springer.com/10.1007/978-3-031-60571-0_1},
	abstract = {The first decade of research and thinking in learning analytics has seen shifting foci and evolving theoretical foundations. Indeed, the very role of theory in, about, and of learning analytics has been addressed in different ways across subsections of the field. From an early emphasis on data, computing and systems, the field has increasingly connected with theories and ideas from educational research, sociology, philosophy, and the learning sciences. The richness resulting from this confluence of theories provides a foundation for enhancing the use of data and analytics for learning, differentiating learning analytics from other pre-existing fields, and for deepening the understanding of how learning works. However, despite the broadening scope of theoretical perspectives in, about, and of learning analytics, old tensions remain, and new ones have emerged. As is evident in other areas of educational research, there are intractable differences in fundamental philosophies that create barriers to meaningful dialogue and the progression of the field. In this chapter, we will provide an overview of the key theoretical trends in learning analytics research and place these trends within a broader perspective. Specifically, we will describe the theorising of learning analytics, theory in learning analytics, and theories about learning analytics.},
	language = {en},
	urldate = {2025-01-01},
	booktitle = {Theory {Informing} and {Arising} from {Learning} {Analytics}},
	publisher = {Springer Nature Switzerland},
	author = {Lodge, Jason M. and Knight, Simon and Kitto, Kirsty},
	editor = {Bartimote, Kathryn and Howard, Sarah K. and Gašević, Dragan},
	year = {2024},
	doi = {10.1007/978-3-031-60571-0_1},
	keywords = {Promotion:Literaturanalyse, Charité:Promotion, Leraning:Analytics},
	pages = {3--18},
	file = {PDF:/Users/jochenhanisch-johannsen/Zotero/storage/F8T6L8NA/Lodge et al. - 2024 - Theory and Learning Analytics, a Historical Perspective.pdf:application/pdf},
}

@book{bartimote_theory_2024-2,
	address = {Cham},
	title = {Theory {Informing} and {Arising} from {Learning} {Analytics}},
	copyright = {https://www.springernature.com/gp/researchers/text-and-data-mining},
	isbn = {978-3-031-60570-3 978-3-031-60571-0},
	url = {https://link.springer.com/10.1007/978-3-031-60571-0},
	language = {en},
	urldate = {2025-01-01},
	publisher = {Springer Nature Switzerland},
	editor = {Bartimote, Kathryn and Howard, Sarah K. and Gašević, Dragan},
	year = {2024},
	doi = {10.1007/978-3-031-60571-0},
	keywords = {Promotion:Literaturanalyse, Charité:Promotion, Leraning:Analytics},
	file = {PDF:/Users/jochenhanisch-johannsen/Zotero/storage/86KQG5WE/Bartimote et al. - 2024 - Theory Informing and Arising from Learning Analytics.pdf:application/pdf},
}

@article{shaffer_transmodal_2025,
	title = {Transmodal {Analysis}},
	copyright = {https://creativecommons.org/licenses/by/4.0},
	issn = {1929-7750},
	url = {https://learning-analytics.info/index.php/JLA/article/view/8423},
	doi = {10.18608/jla.2025.8423},
	abstract = {Learning is a multimodal process, and learning analytics (LA) researchers can readily access rich learning process data from multiple modalities, including audio-video recordings or transcripts of in-person interactions; logfiles and messages from online activities; and biometric measurements such as eye-tracking, movement, and galvanic skin response. While many techniques are used in LA to model different types of learning process data—most of which are state-dependent (or state-space) approaches that model a learning process at any given time as a function of the preceding events—constructing multimodal models has so far relied on fusion of different data streams, which converts multimodal data into a unimodal format. This creates a number of problems for multimodal modelling, the most important of which is that it treats different data modalities as equivalent. That is, existing state-dependent models of fused data cannot easily account for (a) events that may have different impacts on future events based on what those future events are and the context in which they are occurring; (b) how events may influence some groups of learners differently; and (c) which events are visible (and thus potentially impactful) to which students. In this paper, we propose transmodal analysis (TMA), a mathematical and computational framework designed to address these challenges. TMA is not a data analysis method but rather an approach to modelling that can augment existing state-dependent models of learning processes to account for multimodal data without data fusion. We present a conceptual and methodological description of TMA, and we include an appendix with a detailed worked example as a proof of concept. While this approach is in the early stages of development, it has the potential to significantly improve the ease, efficiency, and fairness of multimodal analyses of learning processes.},
	language = {en},
	urldate = {2025-01-30},
	journal = {Journal of Learning Analytics},
	author = {Shaffer, David and Wang, Yeyu and Ruis, Andrew},
	month = jan,
	year = {2025},
	keywords = {Promotion:Ausschluss, Promotion:Literaturanalyse, Charité:Promotion, Leraning:Analytics},
	pages = {1--22},
	file = {PDF:/Users/jochenhanisch-johannsen/Zotero/storage/TT7G2UGA/Shaffer et al. - 2025 - Transmodal Analysis.pdf:application/pdf},
}

@book{hagerbaumer_future_2025,
	address = {Wiesbaden},
	title = {Future {Skills} in {Human} {Resource} {Management} und {Corporate} {Learning}: {Neue} {Perspektiven} durch {Analytics}, {EdTech} und {KI}},
	copyright = {https://www.springernature.com/gp/researchers/text-and-data-mining},
	isbn = {978-3-658-46480-6 978-3-658-46481-3},
	shorttitle = {Future {Skills} in {Human} {Resource} {Management} und {Corporate} {Learning}},
	url = {https://link.springer.com/10.1007/978-3-658-46481-3},
	language = {en},
	urldate = {2025-02-15},
	publisher = {Springer Fachmedien Wiesbaden},
	editor = {Hägerbäumer, Miriam and Thelen, Udo and Renz, André},
	year = {2025},
	doi = {10.1007/978-3-658-46481-3},
	keywords = {Promotion:Literaturanalyse, Charité:Promotion, Leraning:Analytics},
	file = {PDF:/Users/jochenhanisch-johannsen/Zotero/storage/4A5LS7NA/Hägerbäumer et al. - 2025 - Future Skills in Human Resource Management und Corporate Learning Neue Perspektiven durch Analytics.pdf:application/pdf},
}

@article{li_how_2025,
	title = {How instructors use learning analytics: the pivotal role of pedagogy},
	issn = {1042-1726, 1867-1233},
	shorttitle = {How instructors use learning analytics},
	url = {https://link.springer.com/10.1007/s12528-025-09432-w},
	doi = {10.1007/s12528-025-09432-w},
	abstract = {This study fills a gap in knowledge regarding experienced instructors’ use of learning analytics, focusing on differences in their approach, the knowledge and skills they activate, and the development of these knowledge and skills. Through a qualitative analysis of think-aloud interviews with 13 analytics-experienced instructors, two distinct profiles of analytics use emerged. Instructors in the first profile prioritized monitoring student engagement and performance to foster desirable behaviors, using analytics to align students with course expectations. Instructors in the second profile focused on understanding student perceptions of learning, aligning the course design with diverse learning behaviors and needs. To arrive at such use, instructors went beyond mere acquisition of technical knowledge to also integrate pedagogical knowledge into their analytics practices. Lastly, the study uncovered specific learning analytics supports, such as ongoing individual consultations, invaluable for developing the needed technical and pedagogical knowledge. Together, the results of this study reveal the pivotal role of pedagogy in analytics use, calling for refinement of conceptual models and tailoring of practical support for instructors.},
	language = {en},
	urldate = {2025-03-15},
	journal = {Journal of Computing in Higher Education},
	author = {Li, Qiujie and Jung, Yeonji and Wise, Alyssa Friend},
	month = feb,
	year = {2025},
	keywords = {Promotion:Literaturanalyse, Charité:Promotion, Leraning:Analytics},
	file = {PDF:/Users/jochenhanisch-johannsen/Zotero/storage/K5KL4HQ4/Li et al. - 2025 - How instructors use learning analytics the pivotal role of pedagogy.pdf:application/pdf},
}

@article{huang_charting_2025,
	title = {Charting the {Development} of {Collaboration} {Skills} {Through} {Collaborative} {Learning} {Analytics} {Systems}},
	copyright = {https://creativecommons.org/licenses/by/4.0},
	issn = {1929-7750},
	url = {https://learning-analytics.info/index.php/JLA/article/view/8523},
	doi = {10.18608/jla.2025.8523},
	abstract = {Collaboration skills are fundamental to effective collaborative learning, career success, and responsible citizenship. Collaborative learning analytics (CLA) systems hold significant potential in helping students develop these skills by automatically collecting group interaction data, analyzing skill levels, and providing actionable feedback so students can reflect, practise, and improve. Previously, most collaborative feedback systems have focused on improving collaborative processes rather than serving as instructional systems for developing collaboration skills over time. To identify what is needed to navigate toward this new type of tool, our paper proposes an interdisciplinary framework that serves as a guiding compass for designing and evaluating such systems. Through an extensive literature review, we evaluate 15 selected systems through the lens of each element of this framework. We map out the current state of the field and identify four major gaps that need to be addressed to transition from systems that support collaboration to systems that support the development of collaboration skills. These gaps are unexplored collaboration skills, lack of validated indicators, limited modelling techniques, and pedagogical feedback design. Finally, we propose a set of corresponding research agendas to bridge these gaps, providing a forward-looking roadmap for designing effective and actionable CLA systems for collaboration skills development.},
	language = {en},
	urldate = {2025-03-21},
	journal = {Journal of Learning Analytics},
	author = {Huang, Xiaomeng and Ochoa, Xavier},
	month = mar,
	year = {2025},
	keywords = {Promotion:Literaturanalyse, Charité:Promotion, Leraning:Analytics},
	pages = {1--29},
	file = {PDF:/Users/jochenhanisch-johannsen/Zotero/storage/LHY5TEDT/Huang und Ochoa - 2025 - Charting the Development of Collaboration Skills Through Collaborative Learning Analytics Systems.pdf:application/pdf},
}

@article{chen_early_2024,
	title = {Early detection of nasopharyngeal carcinoma through machine-learning-driven prediction model in a population-based healthcare record database},
	volume = {13},
	issn = {2045-7634},
	doi = {10.1002/cam4.7144},
	abstract = {OBJECTIVE: Early diagnosis and treatment of nasopharyngeal carcinoma (NPC) are vital for a better prognosis. Still, because of obscure anatomical sites and insidious symptoms, nearly 80\% of patients with NPC are diagnosed at a late stage. This study aimed to validate a machine learning (ML) model utilizing symptom-related diagnoses and procedures in medical records to predict nasopharyngeal carcinoma (NPC) occurrence and reduce the prediagnostic period.
MATERIALS AND METHODS: Data from a population-based health insurance database (2001-2008) were analyzed, comparing adults with and without newly diagnosed NPC. Medical records from 90 to 360 days before diagnosis were examined. Five ML algorithms (Light Gradient Boosting Machine [LGB], eXtreme Gradient Boosting [XGB], Multivariate Adaptive Regression Splines [MARS], Random Forest [RF], and Logistics Regression [LG]) were evaluated for optimal early NPC detection. We further use a real-world data of 1 million individuals randomly selected for testing the final model. Model performance was assessed using AUROC. Shapley values identified significant contributing variables.
RESULTS: LGB showed maximum predictive power using 14 features and 90 days before diagnosis. The LGB models achieved AUROC, specificity, and sensitivity were 0.83, 0.81, and 0.64 for the test dataset, respectively. The LGB-driven NPC predictive tool effectively differentiated patients into high-risk and low-risk groups (hazard ratio: 5.85; 95\% CI: 4.75-7.21). The model-layering effect is valid.
CONCLUSIONS: ML approaches using electronic medical records accurately predicted NPC occurrence. The risk prediction model serves as a low-cost digital screening tool, offering rapid medical decision support to shorten prediagnostic periods. Timely referral is crucial for high-risk patients identified by the model.},
	language = {eng},
	number = {7},
	journal = {Cancer Medicine},
	author = {Chen, Jeng-Wen and Lin, Shih-Tsang and Lin, Yi-Chun and Wang, Bo-Sian and Chien, Yu-Ning and Chiou, Hung-Yi},
	month = apr,
	year = {2024},
	pmid = {38545735},
	pmcid = {PMC10973879},
	keywords = {Adult, Humans, machine learning, Machine Learning, Early Detection of Cancer, Delivery of Health Care, nasopharyngeal carcinoma, Promotion:Ausschluss, Promotion:Literaturanalyse, Charité:Promotion, head and neck cancer, Nasopharyngeal Carcinoma, Nasopharyngeal Neoplasms, prediagnostic, Promotion:S19},
	pages = {e7144},
	file = {Volltext:/Users/jochenhanisch-johannsen/Zotero/storage/BL7MUW8L/Chen et al. - 2024 - Early detection of nasopharyngeal carcinoma through machine-learning-driven prediction model in a po.pdf:application/pdf},
}
