A hybrid model for predicting academic performance of engineering undergraduates

• Published in: International journal of modeling, simulation and scientific computing Vol. 14; no. 2
• Main Authors: Niu, Ke, Jia, BaoTing, Zhou, YuHang, Lu, GuoQiang
• Format: Journal Article
• Language: English
• Published: Hackensack World Scientific Publishing Company 01.04.2023; World Scientific Publishing Co. Pte., Ltd
• Subjects: Academic achievement; Algorithms; Colleges & universities; Datasets; Engineering; Engineering education; Machine learning; Model accuracy; Performance prediction; Prediction models; Radial basis function; Research Article; Students; RBF-Naive Bayes; machine learning; Academic performance prediction
• ISSN: 1793-9623; 1793-9615
• DOI: 10.1142/S1793962323500307

Predicting engineering students’ academic performance is crucial for teachers to implement teaching interventions. How to use students’ behavior records to predict grades during school has attracted extensive attention from researchers. Various machine learning methods have been proposed by previous researchers to predict engineering students’ academic performance through their learning records. However, one of the major challenges now is that many researchers ignore the problem of reduced accuracy of prediction models due to imbalanced data. Simultaneously, a single classifier is susceptible to data changes and has an unstable classification performance. To this end, this paper proposes the RBF-Naive Bayes (RBFNB) prediction model to predict students’ academic performance, which integrates Radial Basis Function (RBF) Network algorithm and Naive Bayes algorithm. The method of RBF Network is applied to predict the academic performance of students and generate prediction results. Then, the method of Naive Bayes is adopted to correct the previous prediction results for improving the accuracy of the prediction. The proposed RBFNB prediction model was applied to the data set about the engineering students’ academic performance from a university and the Kalboard 360 public data set, respectively.