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Providing personalized learning guidance in MOOCs by multi-source data analysis

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Abstract

Although millions of students have access to varieties of learning materials in Massive Open Online Courses (MOOCs), many of them feel lost or isolated in their learning experience. One of the potential reasons is the lack of interactions and guidance for individuals. Since MOOC students have diverse learning objectives, we propose to design different strategies for those students with different engagement styles. In this paper, we provide personalized learning guidance for MOOC students based on multi-source data analysis. We first conduct content analysis to identify key concepts in the courses. We then propose two structured model to evaluate student knowledge states by their quiz submissions. We also study on student learning behaviors and design a dropout prediction system. The experiments show the effectiveness of our algorithms and we discuss on the result both quantitatively and qualitatively. Last but not least, we employ a Web application of online student assessment service for both students and instructors, in order to display student learning states and provide suggestion for individuals.

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  4. 6 https://www.hcharts.cn/

  5. 7 http://jquery.com

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Acknowledgements

This paper is partially supported by the National Natural Science Foundation of China (NSFC Grant Nos.61472006, 61772039, and 91646202).

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Authors and Affiliations

  1. School of Electronics Engineering and Computer Science, Peking University, Beijing, 100871, China

    Ming Zhang, Jile Zhu, Zhuo Wang & Yunfan Chen

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  1. Ming Zhang
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Correspondence to Ming Zhang.

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This article belongs to the Topical Collection: Special Issue on Social Media and Interactive Technologies

Guest Editors: Timothy K. Shih, Lin Hui, Somchoke Ruengittinun, and Qing Li

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Zhang, M., Zhu, J., Wang, Z. et al. Providing personalized learning guidance in MOOCs by multi-source data analysis. World Wide Web 22, 1189–1219 (2019). https://doi.org/10.1007/s11280-018-0559-0

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