Abstract
With the development of the Internet, e-learning has become a new trend for education. However, unlike traditional learning that is face-to-face, e-learning systems construct an environment where learners control their learning process. Many issues have occurred in online learning systems, such as low efficiency, high dropout rates, poor grades and so on. One of the leading causes is students’ low interest in e-learning content, and they cannot find attractive learning materials. Learning resource recommendations can solve this problem by recommending materials that learners may like. However, traditional recommendation methods omit that user’s identity as a student and face underperformance. In this paper, a new learning resource recommendation method based on Online Learning Style is proposed. By integrating learning style characteristics into collaborative filtering algorithm with association rules mining, experimental results showed that the proposed method achieved 25% improvement compared to the method without learners’ features.
Supported by National Natural Science Foundation of China (No. 61977003).
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Yan, L., Yin, C., Chen, H., Rong, W., Xiong, Z., David, B. (2021). Learning Resource Recommendation in E-Learning Systems Based on Online Learning Style. In: Qiu, H., Zhang, C., Fei, Z., Qiu, M., Kung, SY. (eds) Knowledge Science, Engineering and Management. KSEM 2021. Lecture Notes in Computer Science(), vol 12817. Springer, Cham. https://doi.org/10.1007/978-3-030-82153-1_31
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