Student Performance Early Warning based on Data Mining

Abstract

Student performance in higher education is related to many complicated factors and always has uncertainty, so early warning of it is a very difficult issue. In this study, a systematic review was first carried out on student performance prediction and early warning using data mining techniques, including basic data sources, evaluating factors, predicting methods, application tools, and practices. Then, insufficiencies of the related studies were discussed, including incomprehensive source data, inadaptable and unspecialized calculation methods, and lack of integrated methodology systems in practice. Finally, a solution design was proposed, consisting of learning situation big data, a systematic early warning model, and an integrated information support system. Preliminary experiment results showed that it could identify at-risk students in a timely manner and improve the overall efficiency and effectiveness of early warning education management in practice, so it is of both academic and practical significance in promoting the deep integration of information technology and early warning education.

Predictive Analysis of Computer Science Student Performance: An ACM2013 Knowledge Area Approach

“…Their adherence to the CRISP-DM methodology shows a structured approach to predictive modeling, from understanding the educational field to evaluating models against established metrics such as RMSE and MAE. The success of the XGBoost model in their research, marked by its low MAE and high R 2 , is beneficial for our study, suggesting the potential of advanced ensemble techniques in educational settings.…”