Analyzing Learning Styles using Behavioral Indicators in Web based Learning Environments

Abstract : It is argued that the analysis of the learner’s generated log files during interactions with a learning environment is necessary to produce interpretative views of their activities. The analysis of these log files, or traces, provides "knowledge" about the activity we call indicators. Our work is related to this research field. We are particularly interested in automatically identifying learners’ learning styles from learning indicators. This concept, used in several Educational Hypermedia Systems (EHS) as a criterion for adaptation and tracking, belongs to a set of behaviors and strategies in how to manage and organize information. In this paper, we validate our approach of auto-detection of student's learning styles based on their navigation behavior using machine-learning classifiers.
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Conference papers
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https://hal.archives-ouvertes.fr/hal-01291276
Contributor : Lip6 Publications <>
Submitted on : Monday, March 21, 2016 - 11:43:37 AM
Last modification on : Thursday, March 21, 2019 - 1:10:29 PM

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  • HAL Id : hal-01291276, version 1

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Nabila Bousbia, Jean-Marc Labat, Amar Balla, Issam Rebaï. Analyzing Learning Styles using Behavioral Indicators in Web based Learning Environments. EDM 2010 International Conference on Educational Data Mining, Jun 2010, Pittsburgh, United States. pp.279-280. ⟨hal-01291276⟩

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