Exploring the role of machine learning in forecasting student performance in education: An in-depth review of literature

Abstract

The literature on the role of machine learning in forecasting student performance in education lacks comprehensive data quality, equitable and interpretable models, consideration of contextual and causal factors, and the integration of human expertise. This review will explore machine learning types, algorithms, predictive performance, and impact on student performance in education. A systematic literature review based on articles published in the past 2019–2023 period Methods: The IEEE Xplore database was searched by using keywords such as "Educational Data Mining," "Student Performance Prediction," "Evaluations of Students," "Performance Analysis of Students," and "Learning Curve Prediction." were employed and 50 papers were selected. Results: The analysis of the results highlighted prominent patterns. Half of the studies favored supervised learning methods, with decision trees leading (19 instances), followed by Long Short-Term Memory and Random Forest (16 each), and K-Nearest Neighbor and Naive Bayes (12 and 11 times). Support Vector Machine and Logistic Regression were noted 10 and 9 times, respectively. Noteworthy were ANN, CNN, and Xgboost. Positive impacts were evident in 36 cases; only one showed negative effects, while 13 indicated intricate relationships. This study helped in understanding the prevalent machine learning methods used for predicting student performance, provides a benchmark for assessing the effectiveness of new or alternative techniques. Conclusion: This review highlights the varied machine learning uses in predicting student performance in education, emphasizing supervised methods, diverse algorithms, and complex intervention impacts.