Analyzing learner characteristics and courses based on cognitive abilities, learning styles, and context

Moushir M. El-Bishouty, Ting Wen Chang, Renan Lima, Mohamed B. Thaha, Kinshuk, Sabine Graf

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

6 Citations (Scopus)


Student modeling and context modeling play an important role in adaptive and smart learning systems, enabling such systems to provide courses and recommendations that fit students’ characteristics and consider their current context. In this chapter, three approaches are presented to automatically analyze learners’ characteristics and courses in learning systems based on learners’ cognitive abilities, learning styles, and context. First, a framework and a system are presented to automatically identify students’ working memory capacity (WMC) based on their behavior in a learning management system. Second, a mechanism and an interactive tool are described for analyzing course contents in learning management systems (LMSs) with respect to students’ learning styles. Third, a framework and an application are presented that build a comprehensive context profile through detecting available features of a device and tracking the usage of these features. All three approaches contribute toward building a foundation for providing learners with intelligent, adaptive, and personalized support based on their cognitive abilities, learning styles, and context.

Original languageEnglish
Title of host publicationLecture Notes in Educational Technology
Number of pages23
Publication statusPublished - 2015

Publication series

NameLecture Notes in Educational Technology
ISSN (Print)2196-4963
ISSN (Electronic)2196-4971


  • Cognitive abilities
  • Context profile
  • Learning styles
  • Personalization
  • Student modeling


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