ProbSAP: A comprehensive and high-performance system for student academic performance prediction. (May 2023)

• Record Type: Journal Article
• Authors: Wang, Xinning; Zhao, Yuben; Li, Chong; Ren, Peng
• Is Part Of: Pattern recognition. Volume 137(2023)
• Publication Date: 2023-05
• Subjects: Student academic performance -- SAP prediction -- Educational data mining (EDM) -- Imbalanced data management -- XGBoost-Enhanced method; Pattern perception -- Periodicals; Perception des structures -- Périodiques; Patroonherkenning; 006.4
• DOI: 10.1016/j.patcog.2023.109309
• Languages: English
• ISSNs: 0031-3203

Abstract

Highlights: This paper proposes a novel student academic performance prediction framework (ProbSAP) that can efficiently extract more student characteristics from imbalanced datasets. ProbSAP consists of three components termed as collaborative data processing, scalable metadata clustering, and XGBoost-enhanced SAP prediction. Experimental results of course final mark prediction demonstrate encouraging prediction accuracy and efficiency of ProbSAP on imbalanced data in comparison to other state-of-the-art algorithms. Abstract: The student academic performance prediction is becoming an indispensable service in the computer supported intelligent education system. But conventional machine learning-based methods can only exploit the sparse discriminative features of student behaviors in imbalanced academic datasets to predict student academic performance (SAP). Furthermore, there is a lack of imbalanced data processing mechanisms that can efficiently capture student characteristics and achievement. Therefore, we propose a comprehensive and high-performance prediction framework to probe SAP characteristics (ProbSAP) on massive educational data, which can resolve imbalanced data issue and improve academic prediction performance for making course final mark prediction. It consists of three main components: collaborative data processing module for enhancing the data quality, scalable metadata clustering module for alleviating the imbalance of academic features, and XGBoost-enhanced SAP prediction module for academic performance forecasting. The collaborative data processing module integrates multi-dimensional academic data, which sustains a good supply for clustering and modeling in the ProbSAP framework. The comparative evaluation results demonstrate that ProbSAP delivers superior accuracy and efficiency improvement for the course final mark prediction of college students over other state-of-the-art methods such as CNN, SVR, RFR, XGBoost, Catboost-SHAP, and AS-SAN. On average, ProbSAP reduces the mean absolute error (MAE) by 84.76%, 72.11%, and 66.49% compared with XGBoost, Catboost-SHAP, and AS-SAN, respectively. It also leads to a better out-sample fit that minimizes prediction errors between 1% and 9% with over 98% of actual samples.