Practical early prediction of students' performance using machine learning and eXplainable AI
- Authors
- Jang, Yeonju; Choi, Seongyune; Jung, Heeseok; Kim, Hyeoncheol
- Issue Date
- 11월-2022
- Publisher
- SPRINGER
- Keywords
- Learning performance prediction; Early Prediction; Artificial intelligence in education; Educational data mining; Explainable AI in education
- Citation
- EDUCATION AND INFORMATION TECHNOLOGIES, v.27, no.9, pp.12855 - 12889
- Indexed
- SSCI
SCOPUS
- Journal Title
- EDUCATION AND INFORMATION TECHNOLOGIES
- Volume
- 27
- Number
- 9
- Start Page
- 12855
- End Page
- 12889
- URI
- https://scholar.korea.ac.kr/handle/2021.sw.korea/143263
- DOI
- 10.1007/s10639-022-11120-6
- ISSN
- 1360-2357
- 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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Collections - Graduate School > Department of Computer Science and Engineering > 1. Journal Articles
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