TY - JOUR
T1 - Practical early prediction of students’ performance using machine learning and eXplainable AI
AU - Jang, Yeonju
AU - Choi, Seongyune
AU - Jung, Heeseok
AU - Kim, Hyeoncheol
N1 - Funding Information:
This work was supported by the National Research Foundation (NRF), Korea, under the project BK21 FOUR.
Publisher Copyright:
© 2022, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.
PY - 2022/11
Y1 - 2022/11
N2 - 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.
AB - 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.
KW - Artificial intelligence in education
KW - Early Prediction
KW - Educational data mining
KW - Explainable AI in education
KW - Learning performance prediction
UR - http://www.scopus.com/inward/record.url?scp=85131700415&partnerID=8YFLogxK
U2 - 10.1007/s10639-022-11120-6
DO - 10.1007/s10639-022-11120-6
M3 - Article
AN - SCOPUS:85131700415
SN - 1360-2357
VL - 27
SP - 12855
EP - 12889
JO - Education and Information Technologies
JF - Education and Information Technologies
IS - 9
ER -