Abstract
Applying learning analytics to generate personalized learning paths is getting popular in recent research. This study designs a Moodle plug-in called Personalized Study Guide that can generate personalized learning paths according to students’ learning styles. The plug-in supports two ways to determine students’ learning styles: one is using Inventory of Learning Styles (ILS), the well-established learning style questionnaire developed by Felder and Silverman, and the other is through the analysis of students’ past behaviour patterns on Moodle. The Personalized Study Guide analyzes the learning-style weightings for each learning resources/activities in the course. Using the calculated learning-style weightings, the Personalized Study Guide could determine which learning resources/activities are closer to students’ learning styles to generate the recommended learning path. The research team is conducting the experiment to evaluate the perceived usefulness of the Personal Study Guide in 2023. The details of the evaluation plan are also described in this study.
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Acknowledgements
The authors acknowledge the support of the National Science and Engineering Research Council of Canada (NSERC) [RGPIN-2018-05545].
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Krahn, T., Kuo, R., Chang, M. (2023). Personalized Study Guide: A Moodle Plug-in Generating Personal Learning Path for Students. In: Frasson, C., Mylonas, P., Troussas, C. (eds) Augmented Intelligence and Intelligent Tutoring Systems. ITS 2023. Lecture Notes in Computer Science, vol 13891. Springer, Cham. https://doi.org/10.1007/978-3-031-32883-1_30
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