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Open Access

Enhanced Learning Resource Recommendation Based on Online Learning Style Model

School of Computer Science and Engineering, Beihang University, Beijing 100191, China.
Sino-French Engineer School, Beihang University, Beijing 100191, China.
Laboratoire d’InfoRmatique en Image et Systèmes d’information (LIRIS) Lab, Ecole Centrale de Lyon, Ecully, 69134, France.
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Abstract

Smart learning systems provide relevant learning resources as a personalized bespoke package for learners based on their pedagogical needs and individual preferences. This paper introduces a learning style model to represent features of online learners. It also presents an enhanced recommendation method named Adaptive Recommendation based on Online Learning Style (AROLS), which implements learning resource adaptation by mining learners’ behavioral data. First, AROLS creates learner clusters according to their online learning styles. Second, it applies Collaborative Filtering (CF) and association rule mining to extract the preferences and behavioral patterns of each cluster. Finally, it generates a personalized recommendation set of variable size. A real-world dataset is employed for some experiments. Results show that our online learning style model is conducive to the learners’ data mining, and AROLS evidently outperforms the traditional CF method.

Keywords

smart learninge-learningonline learning styleadaptive recommendationCollaborative Filtering (CF)

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Tsinghua Science and Technology
Volume 25 Issue 3,
June 2020
Pages 348-356
DOI: 10.26599/TST.2019.9010014
Cite this article:
Chen H, Yin C, Li R, et al. Enhanced Learning Resource Recommendation Based on Online Learning Style Model. Tsinghua Science and Technology, 2020, 25(3): 348-356. https://doi.org/10.26599/TST.2019.9010014

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Received: 04 March 2019
Accepted: 04 April 2019
Published: 07 October 2019
© The author(s) 2020

The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).
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