Learning Analytics: Challenges and Future Research Directions

Authors

  • Mohamed Amine Chatti RWTH Aachen University , Informatik 9 (Learning Technologies)
  • Vlatko Lukarov RWTH Aachen University , Informatik 9 (Learning Technologies)
  • Hendrik Thüs RWTH Aachen University , Informatik 9 (Learning Technologies)
  • Arham Muslim RWTH Aachen University , Informatik 9 (Learning Technologies)
  • Ahmed Mohamed Fahmy Yousef RWTH Aachen University , Informatik 9 (Learning Technologies)
  • Usman Wahid RWTH Aachen University , Informatik 9 (Learning Technologies)
  • Christoph Greven RWTH Aachen University , Informatik 9 (Learning Technologies)
  • Arnab Chakrabarti RWTH Aachen University , Informatik 9 (Learning Technologies)
  • Ulrik Schroeder RWTH Aachen University , Informatik 9 (Learning Technologies)

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.

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Published

2014-11-06

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Section

Articles

URN