Automatic detection of learning styles based on dynamic Bayesian network in adaptive e-learning system

Lamia Mahnane and Mohamed Hafidi

International Journal of Innovation and Learning, 2016, vol. 20, issue 3, 289-308

Abstract: A large number of studies attest that learning is facilitated if the teaching strategies are in accordance with the students learning styles (LS), making the learning process more effective and considerably improving student's performances. But, traditional approaches for detection of LS are inefficient. This work determines the current preferences through dynamic Bayesian network that represent the matches between LS and teaching strategies in order to determine how much a given strategy is interesting to a student. The LS theory that supports this approach is the LS model proposed by Felder-Silverman's learning styles model (FSLSM). Our approach gradually and constantly adjusts the student model, taking into account students' performances, student's effort, student's intensity, student's resistance and student's attention. Promising results were obtained from experiments, and some of them are discussed in this paper.

Keywords: dynamic Bayesian networks; learning styles; teaching strategy; adaptive e-learning; online learning; electronic learning; student performance; automatic detection; Felder-Silverman. (search for similar items in EconPapers)
Date: 2016
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.inderscience.com/link.php?id=79067 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:ids:ijilea:v:20:y:2016:i:3:p:289-308

Access Statistics for this article

More articles in International Journal of Innovation and Learning from Inderscience Enterprises Ltd
Bibliographic data for series maintained by Sarah Parker ().