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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:0009-5-40350
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Bibtex

@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"
}
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RIS

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  -
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Wordbib

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ISI

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.
ER
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    <topic>open assessment</topic>
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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
ddc
  • , 003.54 information theory
  • , 005.7 data in computer systems
  • , 005.74 data files and databases
  • , 153.15 learning
  • , 371.358 electronic distance education
  • , 378 higher education
pacs
  • , C1260 information theory
  • , C6160 database management systems
  • , C6170K knowledge engineering techniques
DDC 370
Rights fDPPL
URN: urn:nbn:de:0009-5-40350