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Key Action Extraction for Learning Analytics

  • Maren Scheffel
  • Katja Niemann
  • Derick Leony
  • Abelardo Pardo
  • Hans-Christian Schmitz
  • Martin Wolpers
  • Carlos Delgado Kloos
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7563)

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.

Keywords

Usage data learning analytics self-regulated learning activity patterns 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Maren Scheffel
    • 1
  • Katja Niemann
    • 1
  • Derick Leony
    • 2
  • Abelardo Pardo
    • 2
  • Hans-Christian Schmitz
    • 1
  • Martin Wolpers
    • 1
  • Carlos Delgado Kloos
    • 2
  1. 1.Fraunhofer Institute for Applied Information Technology FIT, Schloss BirlinghovenSankt AugustinGermany
  2. 2.Universidad Carlos III de MadridLeganés (Madrid)Spain

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