Abstract
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.
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The authors acknowledge the support of nserc, icore, xerox, mitacs, and the research-related gift funding by mr. a. markin.
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El-Bishouty, M.M., Chang, TW., Lima, R., Thaha, M.B., Kinshuk, Graf, S. (2015). Analyzing Learner Characteristics and Courses Based on Cognitive Abilities, Learning Styles, and Context. In: Chang, M., Li, Y. (eds) Smart Learning Environments. Lecture Notes in Educational Technology. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-44447-4_1
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