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Predicting Student Rankings Based on the Dual-Student Performance Comparison Model

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Data Science (ICPCSEE 2022)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1629))

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Abstract

Currently, learning early warning mainly uses two methods, student classification and performance regression, both of which have some shortcomings. The granularity of student classification is not fine enough. The performance regression gives an absolute score value, and it cannot directly show the position of a student in the class. To overcome the above shortcomings, we will focus on a rare learning early warning method — ranking prediction. We propose a dual-student performance comparison model (DSPCM) to judge the ranking relationship between a pair of students. Then, we build the model using data including class quiz scores and online behavior times and find that these two sets of features improve the Spearman correlation coefficient for the ranking prediction by 0.2986 and 0.0713, respectively. We also compare the process proposed with the method of first using a regression model to predict scores and then ranking students. The result shows that the Spearman correlation coefficient of the former is 0.1125 higher than that of the latter. This reflects the advantage of the DSPCM in ranking prediction.

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Authors and Affiliations

  1. School of Software and Microelectronics, Peking University, Beijing, China

    Yijie Chen & Zhengzhou Zhu

Authors
  1. Yijie Chen
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  2. Zhengzhou Zhu
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Corresponding author

Correspondence to Yijie Chen .

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Editors and Affiliations

  1. Southwest Petroleum University, Chengdu, China

    Yang Wang

  2. University of Electronic Science and Technology of China, Chengdu, China

    Guobin Zhu

  3. Harbin Engineering University, Harbin, China

    Qilong Han

  4. Southwest Petroleum University, Chengdu, China

    Liehui Zhang

  5. Harbin University of Science and Technology, Harbin, China

    Xianhua Song

  6. National Academy of Guo Ding Institute of Data Sciences, Beijing, China

    Zeguang Lu

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Chen, Y., Zhu, Z. (2022). Predicting Student Rankings Based on the Dual-Student Performance Comparison Model. In: Wang, Y., Zhu, G., Han, Q., Zhang, L., Song, X., Lu, Z. (eds) Data Science. ICPCSEE 2022. Communications in Computer and Information Science, vol 1629. Springer, Singapore. https://doi.org/10.1007/978-981-19-5209-8_21

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  • DOI: https://doi.org/10.1007/978-981-19-5209-8_21

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-19-5208-1

  • Online ISBN: 978-981-19-5209-8

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