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Feature Engineering for Learning Analytics in Higher Education Research on Motivation and Self-Regulation

Sat, April 26, 3:20 to 4:50pm MDT (3:20 to 4:50pm MDT), The Colorado Convention Center, Floor: Meeting Room Level, Room 607

Session Type: Symposium

Abstract

Learning analytics is still a rapidly growing field. In connection with other aspects of digitalisation, it has the potential to change learning in the long term by allowing new facets of learning to be examined and teachers and learners to benefit directly from these insights, for example in feedback. However, the potential of learning analytics relies heavily on the process of feature engineering. Only theoretically informed and empirically valid features can advance learning research and practice. This can be exemplified by research into motivation and self-regulation. This symposium therefore tests the robustness of features obtained from university courses by examining their stability across courses, their use in conjunction with relevant theoretical concepts, and their multimodal validation.

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