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Multi-level and multi-perspective visual correlation analysis between general courses and program courses

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Abstract

Exploring the potential impact of important general courses on program-specific courses in universities can help to improve the entire teaching and learning process for an academic major. However, the large number of courses and multiple factors affecting students’ course grades makes it difficult to reveal and analyze the complicated relationship between the two types of courses only from a single perspective or at a single level. Thence, this paper starts with analysis of historical course grades data within an undergraduate program and then presents an interactive visual analytic system, MVCAS, which is designed to demonstrate and explore the various correlations between these two types of courses at different levels and from different perspectives. The major contributions of this work include: (1) a multi-angle preprocessing of course grades data, including decomposition, extraction and conversion; (2) multiple coordinated analysis views which make it possible to effectively explore the overall, categorical and pairwise course correlations and further link courses with students, instructors and semesters together; and (3) a top-down correlation analysis process for general courses and program ones. The effectiveness and usefulness of MVCAS have been preliminarily demonstrated through a case study, in which the field experts use this tool to investigate different levels of correlations between the focused mathematics and program-specific courses in a computer science major comprehensively.

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Acknowledgements

This research is partially supported by National Natural Science Foundation of China (No. 60873093). We further thank all anonymous reviewers for their valuable comments.

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Authors and Affiliations

  1. Beijing Key Laboratory of Petroleum Data Mining, Department of Computer Science, China University of Petroleum, Beijing, China

    Lianen Ji & Yaming Yuan

  2. Research Institute of China Mobile Communications Company Limited, Beijing, China

    Fang Gao

Authors
  1. Lianen Ji
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  2. Yaming Yuan
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  3. Fang Gao
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Correspondence to Lianen Ji.

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Funding

This study was funded by the National Natural Science Foundation of China (Grant No. 60873093).

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Lianen Ji declares that he has no conflict of interest. Yaming Yuan declares that he has no conflict of interest. Fang Gao declares that she has no conflict of interest.

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Ji, L., Yuan, Y. & Gao, F. Multi-level and multi-perspective visual correlation analysis between general courses and program courses. Vis Comput 37, 477–495 (2021). https://doi.org/10.1007/s00371-020-01818-4

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