Title: An automatic adaptive grouping of learners in an e-learning environment based on fuzzy grafting and snap-drift clustering
Authors: Mohammad Sadegh Rezaei; Gholam Ali Montazer
Addresses: Information Technology Department, Tarbiat Modares University, Jalal Ale Ahmad Highway, P.O. Box 14115-179, Tehran, Iran ' Information Technology Department, Tarbiat Modares University, Jalal Ale Ahmad Highway, P.O. Box 14115-179, Tehran, Iran
Abstract: Adaptive learning systems provide e-learning-based educational services tailored to the needs, preferences and capabilities of learners. The quality of services provided by these systems largely depends on their ability to acquire proper description of learners regarding their personality, behaviour and learning style and to categorise these learners accurately into homogeneous and heterogeneous groups. The ability of an adaptive system to provide a suitable course through suitable presentation is influenced by the accuracy of mentioned grouping process. This paper presents a novel adaptive learning system possessing an automatic and intelligent learner grouping capability. The grouping approach used in this system consists of four stages, identifying the group structures, classifying the learners into the identified groups, detecting the expiration of groups and modifying the groups of learners. This clustering concept is developed by modified fuzzy snap-drift method, and the process of assigning suitable content to identified groups is implemented by a decision tree. The proposed system is implemented on an e-learning course to evaluate its effect on the learning quality. The evaluation of 'academic satisfaction' and 'progress' criteria shows that the proposed system has been able to make significant improvements in e-learning environment.
Keywords: adaptive grouping; adaptive learning systems; e-learning; electronic learning; online learning; fuzzy grafting clustering; personality; learner behaviour; learning styles; neural networks; snap-drift clustering; technology enhanced learning; automatic grouping; learner groups; group structures; learner classification; group expiration; group modification.
International Journal of Technology Enhanced Learning, 2016 Vol.8 No.2, pp.169 - 186
Received: 12 Feb 2016
Accepted: 15 Mar 2016
Published online: 28 Jul 2016 *