Key action extraction for learning analytics

Authors:
Maren Scheffel;Katja Niemann;Derick Leony;Abelardo Pardo;Hans-Christian Schmitz;Martin Wolpers;Carlos Delgado Kloos
Affiliations:
Fraunhofer Institute for Applied Information Technology FIT, Schloss Birlinghoven, Sankt Augustin, Germany;Fraunhofer Institute for Applied Information Technology FIT, Schloss Birlinghoven, Sankt Augustin, Germany;Universidad Carlos III de Madrid, Leganés (Madrid), Spain;Universidad Carlos III de Madrid, Leganés (Madrid), Spain;Fraunhofer Institute for Applied Information Technology FIT, Schloss Birlinghoven, Sankt Augustin, Germany;Fraunhofer Institute for Applied Information Technology FIT, Schloss Birlinghoven, Sankt Augustin, Germany;Universidad Carlos III de Madrid, Leganés (Madrid), Spain
Venue:
EC-TEL'12 Proceedings of the 7th European conference on Technology Enhanced Learning
Year:
2012

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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.