A Machine Learning Approach to Predicting Academic Performance in Pennsylvania’s Schools

作者：Shan Chen, Yuanzhao Ding · 发表于：Social Sciences · 年份：2023 · DOI：10.3390/socsci12030118 · 被引用次数：45 · 研究领域：Online Learning and Analytics、Software Engineering Research、Software System Performance and Reliability

Academic performance prediction is an indispensable task for policymakers. Academic performance is frequently examined using classical statistical software, which can be used to detect logical connections between socioeconomic status and academic performance. These connections, whose accuracy depends on researchers’ experience, determine prediction accuracy. To eliminate the effects of logical relationships on such accuracy, this research used ‘black box’ machine learning models extended with education and socioeconomic data on Pennsylvania to predict academic performance in the state. The decision tree, random forest, logistic regression, support vector machine, and neural network achieved testing accuracies of 48%, 54%, 50%, 51%, and 60%, respectively. The neural network model can be used by policymakers to forecast academic performance, which in turn can aid in the formulation of various policies, such as those regarding funding and teacher selection. Finally, this study demonstrated the feasibility of machine learning as an auxiliary educational decision-making tool for use in the future.