Towards Intelligent Adaptive E-Learning Systems - Machine Learning for Learner Activity Classification

Research output: Chapter in Book/Report/Conference proceedingsConference contributionpeer-review

Abstract

As adaptivity in e-learning systems has become popular during the past years, new challenges and potentials have emerged in the field of adaptive systems. Adaptation, traditionally focused on the personalization of content, is now also required for learner communication and cooperation. With the increasing complexity of adaptation tasks, the need for automated processing of usage data, information extraction and pattern detection grows. We present learner activity mining and classification as a basis for adaptation in educational systems and discuss intelligent techniques in this context. Based on real usage data, we present the results of experiments comparing the behaviour and performance of different classification algorithms.
Original languageEnglish
Title of host publication17th Workshop on Adaptivity and User Modeling in Interactive Systems
PublisherTectum Wissenschaftsverlag (Nomos Verlagsgesellschaft)
Pages26-31
Publication statusPublished - 2009
Event17th Workshop on Adaptivity and User Modeling in Interactive Systems (ABIS09) - Darmstadt, Germany
Duration: 21 Sep 200923 Sep 2009

Workshop

Workshop17th Workshop on Adaptivity and User Modeling in Interactive Systems (ABIS09)
Country/TerritoryGermany
CityDarmstadt
Period21.09.200923.09.2009

Keywords

  • Adaptive Intelligent E-Learning Systems
  • Machine Learning
  • Learner Activity Classification
  • Educational Data Mining

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