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Chatti, M. A., Lukarov, V., Thüs, H., Muslim, A., Yousef, A. M. F., Wahid, U., Greven, C., Chakrabarti, A., Schroeder, U. (2014). Learning Analytics: Challenges and Future Research Directions. eleed, Iss. 10. (urn:nbn:de:0009-5-40350)
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%0 Journal Article %T Learning Analytics: Challenges and Future Research Directions %A Chatti, Mohamed Amine %A Lukarov, Vlatko %A Thüs, Hendrik %A Muslim, Arham %A Yousef, Ahmed Mohamed Fahmy %A Wahid, Usman %A Greven, Christoph %A Chakrabarti, Arnab %A Schroeder, Ulrik %J eleed %D 2014 %V 10 %N 1 %@ 1860-7470 %F chatti2014 %X In recent years, learning analytics (LA) has attracted a great deal of attention in technology-enhanced learning (TEL) research as practitioners, institutions, and researchers are increasingly seeing the potential that LA has to shape the future TEL landscape. Generally, LA deals with the development of methods that harness educational data sets to support the learning process. This paper provides a foundation for future research in LA. It provides a systematic overview on this emerging field and its key concepts through a reference model for LA based on four dimensions, namely data, environments, context (what?), stakeholders (who?), objectives (why?), and methods (how?). It further identifies various challenges and research opportunities in the area of LA in relation to each dimension. %L 370 %K context modeling %K e-learning %K educational data mining %K learning analytics %K lifelong learner modeling %K open assessment %K personalization %K seamless learning %U http://nbn-resolving.de/urn:nbn:de:0009-5-40350Download
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@Article{chatti2014, author = "Chatti, Mohamed Amine and Lukarov, Vlatko and Th{\"u}s, Hendrik and Muslim, Arham and Yousef, Ahmed Mohamed Fahmy and Wahid, Usman and Greven, Christoph and Chakrabarti, Arnab and Schroeder, Ulrik", title = "Learning Analytics: Challenges and Future Research Directions", journal = "eleed", year = "2014", volume = "10", number = "1", keywords = "context modeling; e-learning; educational data mining; learning analytics; lifelong learner modeling; open assessment; personalization; seamless learning", abstract = "In recent years, learning analytics (LA) has attracted a great deal of attention in technology-enhanced learning (TEL) research as practitioners, institutions, and researchers are increasingly seeing the potential that LA has to shape the future TEL landscape. Generally, LA deals with the development of methods that harness educational data sets to support the learning process. This paper provides a foundation for future research in LA. It provides a systematic overview on this emerging field and its key concepts through a reference model for LA based on four dimensions, namely data, environments, context (what?), stakeholders (who?), objectives (why?), and methods (how?). It further identifies various challenges and research opportunities in the area of LA in relation to each dimension.", issn = "1860-7470", url = "http://nbn-resolving.de/urn:nbn:de:0009-5-40350" }Download
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TY - JOUR AU - Chatti, Mohamed Amine AU - Lukarov, Vlatko AU - Thüs, Hendrik AU - Muslim, Arham AU - Yousef, Ahmed Mohamed Fahmy AU - Wahid, Usman AU - Greven, Christoph AU - Chakrabarti, Arnab AU - Schroeder, Ulrik PY - 2014 DA - 2014// TI - Learning Analytics: Challenges and Future Research Directions JO - eleed VL - 10 IS - 1 KW - context modeling KW - e-learning KW - educational data mining KW - learning analytics KW - lifelong learner modeling KW - open assessment KW - personalization KW - seamless learning AB - In recent years, learning analytics (LA) has attracted a great deal of attention in technology-enhanced learning (TEL) research as practitioners, institutions, and researchers are increasingly seeing the potential that LA has to shape the future TEL landscape. Generally, LA deals with the development of methods that harness educational data sets to support the learning process. This paper provides a foundation for future research in LA. It provides a systematic overview on this emerging field and its key concepts through a reference model for LA based on four dimensions, namely data, environments, context (what?), stakeholders (who?), objectives (why?), and methods (how?). It further identifies various challenges and research opportunities in the area of LA in relation to each dimension. SN - 1860-7470 UR - http://nbn-resolving.de/urn:nbn:de:0009-5-40350 ID - chatti2014 ER -Download
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PT Journal AU Chatti, M Lukarov, V Thüs, H Muslim, A Yousef, A Wahid, U Greven, C Chakrabarti, A Schroeder, U TI Learning Analytics: Challenges and Future Research Directions SO eleed PY 2014 VL 10 IS 1 DE context modeling; e-learning; educational data mining; learning analytics; lifelong learner modeling; open assessment; personalization; seamless learning AB In recent years, learning analytics (LA) has attracted a great deal of attention in technology-enhanced learning (TEL) research as practitioners, institutions, and researchers are increasingly seeing the potential that LA has to shape the future TEL landscape. Generally, LA deals with the development of methods that harness educational data sets to support the learning process. This paper provides a foundation for future research in LA. It provides a systematic overview on this emerging field and its key concepts through a reference model for LA based on four dimensions, namely data, environments, context (what?), stakeholders (who?), objectives (why?), and methods (how?). It further identifies various challenges and research opportunities in the area of LA in relation to each dimension. ERDownload
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Full Metadata
Bibliographic Citation | e-learning and education, Iss. 10 |
---|---|
Title |
Learning Analytics: Challenges and Future Research Directions (eng) |
Author | Mohamed Amine Chatti, Vlatko Lukarov, Hendrik Thüs, Arham Muslim, Ahmed Mohamed Fahmy Yousef, Usman Wahid, Christoph Greven, Arnab Chakrabarti, Ulrik Schroeder |
Language | eng |
Abstract | In recent years, learning analytics (LA) has attracted a great deal of attention in technology-enhanced learning (TEL) research as practitioners, institutions, and researchers are increasingly seeing the potential that LA has to shape the future TEL landscape. Generally, LA deals with the development of methods that harness educational data sets to support the learning process. This paper provides a foundation for future research in LA. It provides a systematic overview on this emerging field and its key concepts through a reference model for LA based on four dimensions, namely data, environments, context (what?), stakeholders (who?), objectives (why?), and methods (how?). It further identifies various challenges and research opportunities in the area of LA in relation to each dimension. In den letzten Jahren hat Learning Analytics (LA) viel Aufmerksamkeit im Bereich der technology-enhanced learning (TEL) Forschung auf sich gezogen, da Anwender, Institutionen und Forscher zunehmend das Potenzial sehen, das LA hat um die Zukunft der TEL Landschaft zu gestalten. Generell beschäftigt LA sich mit der Entwicklung von Methoden, die Bildungsdatensätze nutzbar zu machen um den Lernprozess zu unterstützen. Dieses Manuskript bietet eine Grundlage für die zukünftige Forschung im Bereich LA. Es bietet einen systematischen Überblick über dieses aufstrebende Gebiet und seine Schlüsselkonzepte durch ein Referenzmodell für LA, welches auf vier Dimensionen basiert, namentlich Daten, Umgebungen und Kontext (Was?), Akteure (Wer?), Ziele (Warum?) und Methoden (Wie?). Darüber hinaus identifiziert es verschiedene Herausforderungen und Forschungsmöglichkeiten im Bereich LA in Bezug auf jede dieser Dimensionen. |
Subject | context modeling, e-learning, educational data mining, learning analytics, lifelong learner modeling, open assessment, personalization, seamless learning |
Classified Subjects |
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DDC | 370 |
Rights | fDPPL |
URN: | urn:nbn:de:0009-5-40350 |