Key action extraction for learning analytics
Date
2012
Authors
Scheffel, M.
Niemann, K.
Leony, D.
Pardo, A.
Schmitz, H.C.
Wolpers, M.
Delgado Kloos, C.
Editors
Ravenscroft, A.
Advisors
Journal Title
Journal ISSN
Volume Title
Type:
Conference paper
Citation
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2012 / Ravenscroft, A. (ed./s), vol.7563, pp.320-333
Statement of Responsibility
Conference Name
7th European Conference on Technology Enhanced Learning (EC-TEL) (18 Sep 2012 - 21 Sep 2012 : Saarbrücken, Germany)
Abstract
Analogous to keywords describing the important and relevant content of a document we extract key actions from learners' usage data assuming that they represent important and relevant parts of their learning behaviour. These key actions enable the teachers to better understand the dynamics in their classes and the problems that occur while learning. Based on these insights, teachers can intervene directly as well as improve the quality of their learning material and learning design. We test our approach on usage data collected in a large introductory C programming course at a university and discuss the results based on the feedback of the teachers.
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Dissertation Note
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Copyright 2012 Springer