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Discovering Knowledge-Point Importance From the Learning-Evaluation Data

Discovering Knowledge-Point Importance From the Learning-Evaluation Data

Hongfei Guo, Xiaomei Yu, Xinhua Wang, Lei Guo, Liancheng Xu, Ran Lu
Copyright: © 2022 |Volume: 20 |Issue: 1 |Pages: 20
ISSN: 1539-3100|EISSN: 1539-3119|EISBN13: 9781799893424|DOI: 10.4018/IJDET.302012
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MLA

Guo, Hongfei, et al. "Discovering Knowledge-Point Importance From the Learning-Evaluation Data." IJDET vol.20, no.1 2022: pp.1-20. http://doi.org/10.4018/IJDET.302012

APA

Guo, H., Yu, X., Wang, X., Guo, L., Xu, L., & Lu, R. (2022). Discovering Knowledge-Point Importance From the Learning-Evaluation Data. International Journal of Distance Education Technologies (IJDET), 20(1), 1-20. http://doi.org/10.4018/IJDET.302012

Chicago

Guo, Hongfei, et al. "Discovering Knowledge-Point Importance From the Learning-Evaluation Data," International Journal of Distance Education Technologies (IJDET) 20, no.1: 1-20. http://doi.org/10.4018/IJDET.302012

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Abstract

As students in online courses usually show differences in their cognitive levels and lack communication with teachers, it is difficult for teachers to grasp student perceptions of the importance of knowledge-points and to develop personalized teaching. Though recent studies have paid attention to this topic, existing methods fail to calculate the importance of every knowledge-point for each student. Moreover, some studies are based on expert analysis, are not data-driven, and hence are inapplicable to large-scale online scenarios. To address these issues, this article proposes a personal topic rank (PTR) as a solution, which links students and concepts to generate a personalized knowledge concept map. Then, the authors present a novel PTR method to calculate the importance of knowledge-points, wherein student mastery of knowledge-points, student understanding, and the knowledge-point itself are considered simultaneously. This article conducts extensive experiments on a real-world dataset to demonstrate that the method can achieve better results than baselines.