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Predicting high-risk students using Internet access logs

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Abstract

Predicting student performance (PSP) is of great use from an educational perspective, especially for high-risk students who need timely help to complete their studies. Previous PSP studies construct prediction models mainly on data collected from questionnaires or some specific learning systems. Instead, students’ Internet access logs were used in this study to predict high-risk students. Since the raw data in log files are high-dimensional, complex and full of noise, several methods were proposed for the preprocessing of the data source. A high-dimensional feature selection framework is then designed to prepare features for the construction of a prediction model with good trade-off between computational efficiency and prediction performance. Experiments showed that the proposed prediction model can identify about 85% of high-risk students. Some online characteristics of high-risk students were also discovered, which might help student counselors and educational researchers better understand the relationship between students’ Internet use and their academic performance.

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Acknowledgements

This research was supported by National Natural Science Foundation of China under Grant No. 61472464, National Natural Science Foundation Project of CQ CSTC (No. cstc2016jcyjA0276) and Fundamental Research Funds for the Central Universities (Nos. 106112016CDJSK04XK09 and 106112016CDJXY180006).

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Authors and Affiliations

  1. College of Computer Science, Chongqing University, Chongqing, 400044, China

    Qing Zhou, Wenjun Quan & Chao Mou

  2. College of Foreign Languages and Cultures, Chongqing University, Chongqing, 400044, China

    Yu Zhong & Wei Xiao

  3. College of Economics and Management, Chongqing University of Posts and Telecommunications, Chongqing, 400065, China

    Yong Wang

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  1. Qing Zhou
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  2. Wenjun Quan
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  3. Yu Zhong
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  4. Wei Xiao
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  5. Chao Mou
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Correspondence to Wenjun Quan.

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Zhou, Q., Quan, W., Zhong, Y. et al. Predicting high-risk students using Internet access logs. Knowl Inf Syst 55, 393–413 (2018). https://doi.org/10.1007/s10115-017-1086-5

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  • DOI: https://doi.org/10.1007/s10115-017-1086-5

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