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

Yeonju Jang, Seongyune Choi, Heeseok Jung, Hyeoncheol Kim

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish
JournalEducation and Information Technologies
DOIs
Publication statusAccepted/In press - 2022

Keywords

  • Artificial intelligence in education
  • Early Prediction
  • Educational data mining
  • Explainable AI in education
  • Learning performance prediction

ASJC Scopus subject areas

  • Education
  • Library and Information Sciences

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