Article: Clustering learner profiles based on usage data in adaptive e-learning Journal: International Journal of Knowledge and Learning (IJKL) 2016 Vol.11 No.1 pp.24 - 41 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. Inderscience Publishers - linking academia, business and industry through research

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

Authors: Sucheta V. Kolekar; Radhika M. Pai; M.M. Manohara Pai

Addresses: Department of Information and Communication Technology, Manipal Institute of Technology, Manipal University, Manipal, Karnataka 576104, India ' Department of Information and Communication Technology, Manipal Institute of Technology, Manipal University, Manipal, Karnataka 576104, India ' Department of Information and Communication Technology, Manipal Institute of Technology, Manipal University, Manipal, Karnataka 576104, India

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.

Keywords: weblog analysis; blogs; Felder-Silverman learning styles; adaptive e-learning; sequence similarity; fuzzy clustering; learner profiles; usage data; electronic learning; online learning; learning behaviour.

DOI: 10.1504/IJKL.2016.078650

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

Received: 27 Jul 2015
Accepted: 23 Feb 2016
Published online: 30 Aug 2016 *

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

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