[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120669-en":3,"doc-seo-120669-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},120669,1099513958762,"Logic","https://ap-avatar.wpscdn.com/avatar/1000023916a998db790?x-image-process=image/resize,m_fixed,w_180,h_180&k=1784791008015729253",8,"Research & Report","A Machine Learning Approach to Predicting Academic Performance in Pennsylvania's Schools","Academic performance prediction supports policymakers in targeting education resources and reducing gaps tied to socioeconomic differences. Traditional approaches using classical statistical software rely on interpretable relationships between socioeconomic status and outcomes, with accuracy dependent on researchers’ experience. This study applies black-box machine learning models using education and socioeconomic data from Pennsylvania to predict academic performance. A decision tree, random forest, logistic regression, support vector machine, and neural network were tested, achieving accuracies of 48%, 54%, 50%, 51%, and 60% respectively, with the neural network enabling actionable forecasting for policy decisions and demonstrating machine learning’s feasibility as an auxiliary tool.","social sciences   \nArticle  \nA Machine Learning Approach to Predicting Academic Performance in Pennsylvania's Schools  \nShan Chen 1 and Yuanzhao Ding 2, *  \nCitation: Chen, Shan, and Yuanzhao Ding. 2023. A Machine Learning Approach to Predicting Academic Performance in Pennsylvania's Schools. Social Sciences 12: 118 .  \n[https://doi.org/10.3390/](https://doi.org/10.3390/)[ ](https://doi.org/10.3390/)socsci12030118  \nAcademic Editor: Neil Gilbert  \nReceived: 21 December 2022  \nRevised: 14 February 2023  \nAccepted: 15 February 2023  \nPublished: 24 February 2023  \nCopyright: © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([https://](https://)[ ](https://)[creativecommons.org/licenses/by/](creativecommons.org/licenses/by/)[ ](creativecommons.org/licenses/by/)[4.0/](4.0/)) .  \n1 Department of Applied Social Sciences, The Hong Kong Polytechnic University, 11 Yuk Choi Rd., Hung Hom, Hong Kong, China  \n2 School of Geography and the Environment, University of Oxford, South Parks Road, Oxford OX1 3QY, UK  \n* Correspondence: [armstrongding85@gmail.com](armstrongding85@gmail.com)  \nAbstract: 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.  \nKeywords: machine learning; neural network; socioeconomic status; population; crime rate; academic performance  \n1. Introduction  \nFor a considerable duration, educational systems have widely utilized standardized examinations as a large-scale means of effectively sorting students. When it comes to evaluation efficiency, standardized test scores are overwhelmingly superior in identifying talent over other qualities that schools ought to place greater emphasis on, such as moral character, life adaptability, non-cognitive skills, and social responsibility (Ebel and Frisbie 1972) . These preferences are rooted in the strengths of standardized tests, which are a product of historical and social conventions. There are considerable and obvious advantages to employing paper-and-pencil examinations that feature a series of archetypal questions, including practicality, reliability, good content validity, convenience, accessibility, and openness.  \nDespite the usefulness of traditional tests in assessing students' knowledge and skills, there are several other factors that can impact academic performance, often overlooked. One significant factor identified in predictive studies is socioeconomic status (SES), which plays a vital role in widening the academic performance gap between students in rural and urban institutions (Ramos et al. 2012) . In some European countries, high SES often correlates with above-average exam scores, highlighting the significant impact of SES on educational performance (Jana et al. 2006; Willms et al. 2006) . Conversely, in eastern Europe, low SESand students from rural schools can negatively affect academi","cbCaikdQvsFHr2Ml","https://ap.wps.com/l/cbCaikdQvsFHr2Ml","pdf",3792734,1,13,"English","en",105,"# Abstract\n# Introduction\n# Methodology\n## Data and Features\n## Models and Evaluation\n# Results\n## Prediction Accuracy by Model\n# Discussion\n# Conclusion","[{\"question\":\"Why is academic performance prediction important for policymakers?\",\"answer\":\"Academic performance prediction helps policymakers forecast outcomes and supports decisions related to funding and teacher selection.\"},{\"question\":\"How does this study differ from classical statistical approaches?\",\"answer\":\"Instead of relying on interpretable statistical relationships that depend on researchers’ experience, it uses black-box machine learning models with education and socioeconomic data.\"},{\"question\":\"Which machine learning model achieved the highest testing accuracy?\",\"answer\":\"The neural network achieved the highest testing accuracy at 60% among the tested models.\"}]","A Machine Learning Approach to Predicting Academic Performance in Pennsylvania's Schools | 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is academic performance prediction important for policymakers?","Question",{"text":75,"@type":76},"Academic performance prediction helps policymakers forecast outcomes and supports decisions related to funding and teacher selection.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does this study differ from classical statistical approaches?",{"text":80,"@type":76},"Instead of relying on interpretable statistical relationships that depend on researchers’ experience, it uses black-box machine learning models with education and socioeconomic data.",{"name":82,"@type":73,"acceptedAnswer":83},"Which machine learning model achieved the highest testing accuracy?",{"text":84,"@type":76},"The neural network achieved the highest testing accuracy at 60% among the tested 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