Clustering learner profiles based on usage data in adaptive e-learning

Sucheta V. Kolekar, Radhika M. Pai, Manohara Pai

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

Adaptive e-learning systems enhance the efficiency of online education by providing personalised, adaptive contents and user interfaces which change with respect to learner's requirements. In order to understand the learner's requirements, learners with similar learning behaviour have to be grouped into clusters based on the usage data of each learner. In this paper, a clustering technique to group learner's profiles is proposed where learners will be grouped based on similar sequences of accesses to learning material and time spent. A learner's model is designed based on Felder and Silverman learning style model. The clustering algorithm has two different phases, where the first phase considers the all sequences of access of learners which are in the chronological order of accessing the learning components and learning materials on the portal. The second phase considers the time spent on each learning components as a fuzzy membership function and groups the similar sequences of learners into three clusters. Learners in the clusters have similar learning behaviour for providing adaptive interfaces and contents.

Original languageEnglish
Pages (from-to)24-41
Number of pages18
JournalInternational Journal of Knowledge and Learning
Volume11
Issue number1
DOIs
Publication statusPublished - 2016

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electronic learning
learning behavior
learning
user interface
Group
efficiency
education
time

All Science Journal Classification (ASJC) codes

  • Education

Cite this

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Clustering learner profiles based on usage data in adaptive e-learning. / Kolekar, Sucheta V.; Pai, Radhika M.; Pai, Manohara.

In: International Journal of Knowledge and Learning, Vol. 11, No. 1, 2016, p. 24-41.

Research output: Contribution to journalArticle

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