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Practical early prediction of students’ performance using machine learning and eXplainable AI

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Abstract

Predicting students’ performance in advance could help assist the learning process; if “at-risk” students can be identified early on, educators can provide them with the necessary educational support. Despite this potential advantage, the technology for predicting students’ performance has not been widely used in education due to practical limitations. We propose a practical method to predict students’ performance in the educational environment using machine learning and explainable artificial intelligence (XAI) techniques. We conducted qualitative research to ascertain the perspectives of educational stakeholders. Twelve people, including educators, parents of K-12 students, and policymakers, participated in a focus group interview. The initial practical features were chosen based on the participants’ responses. Then, a final version of the practical features was selected through correlation analysis. In addition, to verify whether at-risk students could be distinguished using the selected features, we experimented with various machine learning algorithms: Logistic Regression, Decision Tree, Random Forest, Multi-Layer Perceptron, Support Vector Machine, XGBoost, LightGBM, VTC, and STC. As a result of the experiment, Logistic Regression showed the best overall performance. Finally, information intended to help each student was visually provided using the XAI technique.

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Acknowledgements

This work was supported by the National Research Foundation (NRF), Korea, under the project BK21 FOUR.

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  1. Dept. of Computer Science and Engineering, The Graduate School of Korea University, Seoul, Republic of Korea

    Yeonju Jang, Seongyune Choi & Heeseok Jung

  2. Dept. of Computer Science and Engineering, Korea University, Seoul, Republic of Korea

    Hyeoncheol Kim

  3. Classting AI Research, Seoul, Republic of Korea

    Heeseok Jung

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Appendix. Features and related literature

Appendix. Features and related literature

Table 8 shows the features that affect student performance and studies in which the features are used. The abbreviated form of the feature names was partially modified to clarify the meaning of each feature (for example, “Medu” was changed to “MotherEducation”). If the feature names used in each study were different for features with the same meaning, they were merged under one name (for example, “low income” and “income” were merged under “income”).

Table 8 Features that may have an impact on a student’s performance and the related literature
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Jang, Y., Choi, S., Jung, H. et al. Practical early prediction of students’ performance using machine learning and eXplainable AI. Educ Inf Technol 27, 12855–12889 (2022). https://doi.org/10.1007/s10639-022-11120-6

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