[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118385-en":3,"doc-seo-118385-105":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},118385,1099514067415,"Rowan","https://ap-avatar.wpscdn.com/avatar/100002539d78ffe74a7?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779092875211072502",8,"Research & Report","Using Machine Learning to Predict Student Outcomes","Predicting students’ performance to identify learners at risk of receiving a D/Fail/Withdraw (DFW) grade supports timely graduation and retention, especially as STEM enrollments decline and STEM graduation rates fall in the United States. The study evaluates early, data-driven grade prediction to enable prompt pedagogical interventions that reduce dropout risk. Machine learning is used to detect patterns for at-risk identification; demographic variables show limited predictive value, while cumulative college GPA and early homework grades strongly correlate with course outcomes, achieving high early-semester accuracy using logistic regression, decision trees, and random forests.","Northern Illinois University  \nHuskie Commons  \n\n| Graduate Research Theses & Dissertations | Graduate Research & Artistry |\n| --- | --- |\n| 2023\u003Cbr>Using Machine Learning to Predict Student Outcomes\u003Cbr>Saba Fatima\u003Cbr>[sabafatima4513@gmail.com](sabafatima4513@gmail.com)\u003Cbr>Follow this and additional works at: [https://huskiecommons.lib.niu.edu/allgraduate-thesesdissertations](https://huskiecommons.lib.niu.edu/allgraduate-thesesdissertations)\u003Cbr> Part of the Physics Commons |  |\n\nRecommended Citation  \nFatima, Saba, \"Using Machine Learning to Predict Student Outcomes\" (2023) . Graduate Research Theses & Dissertations. 7142.  \n[https://huskiecommons.lib.niu.edu/allgraduate-thesesdissertations/7142](https://huskiecommons.lib.niu.edu/allgraduate-thesesdissertations/7142)  \nThis Dissertation/Thesis is brought to you for free and open access by the Graduate Research & Artistry at Huskie Commons. It has been accepted for inclusion in Graduate Research Theses & Dissertations by an authorized administrator of Huskie Commons. For more information, please contact [jschumacher@niu.edu](jschumacher@niu.edu).  \nABSTRACT  \nUSING MACHINE LEARNING TO PREDICT STUDENT OUTCOMES  \nSaba Fatima, Ph.D.  \nDepartment of Physics  \nNorthern Illinois University, 2023  \nMichael Eads, Director  \nPredicting students' performance to identify which students are at risk of receiving a D/Fail/Withdraw (DFW) grade and ensuring their timely graduation is not just desirable but also necessary in most educational entities. In the US, not only is the Science, Technology, Engineering, and Mathematics (STEM) major becoming less popular among students, the graduation rate of STEM students is steadily declining. The lack of STEM graduates in the US is a serious problem that will place this country at a disadvantage as a competitor in international technological advancement. In order to secure its status as a technological leader internationally, the US institutions must be more vigilant in predicting the grades of STEM students to improve student retention in STEM 􀀌elds.  \nUsing early grade prediction allows the school to monitor students' course progress and increases their chances of graduating. Predicting grades is highly bene􀀌cial for at-risk STEM students because it allows for timely pedagogical interventions that can better equip the students for success in their courses and prevent dropouts. Identifying at-risk students is a complicated problem since there are many factors to consider. Traditional approaches to analyzing and using students' data have had mixed results in identifying factors that help predict students' performance early on in the semester. Machine learning can uniquely  \nidentify patterns that other approaches cannot, making it a promising method for grade prediction that is currently available. This study uses machine learning algorithms to identify the key factors that predict which students are at risk early in the semester. The results of this study show that demographic variables such as gender, academic level, and age are of little value in predicting student success. The factors with the highest correlation to the course grade were the students' cumulative college grade point average (GPA) and the grades that the students received on the 􀀌rst four homework assignments of the semester. These variables are provided as input to machine learning algorithms: logistic regression, decision tree, and random forest. Using machine learning, grades can be predicted with 80% to 97% accuracy in the 􀀌rst two to four weeks of the semester, which allows the university to intervene early on.  \nNORTHERN ILLINOIS UNIVERSITY  \nDE KALB, ILLINOIS  \nMAY 2023  \nUSING MACHINE LEARNING TO PREDICT STUDENT OUTCOMES  \nBY  \nSABA FATIMA  \n© 2023 Saba Fatima  \nA DISSERTATION SUBMITTED TO THE GRADUATE SCHOOL IN PARTIAL FULFILLMENT OF THE REQUIREMENTS  \nFOR THE DEGREE  \nDOCTOR OF PHILOSOPHY  \nDEPARTMENT OF PHYSICS  \nDissertation Director:  \nMichael Eads  \nACKNOWLEDGEMENTS  \n","cbCaimXFxWNMqTEb","https://ap.wps.com/l/cbCaimXFxWNMqTEb","pdf",2832673,1,109,"English","en",105,"# Chapter 1 Introduction\n# Chapter 2 Literature Review\n## 2.1 Variables Studied\n## 2.2 Machine Learning vs. Traditional Approaches\n## 2.3 Need for Diverse Data\n## 2.4 Machine Learning Algorithms\n## 2.5 Research Questions\n# Chapter 3 Methodology\n## 3.1 Data Collection\n## 3.2 Study 1: Institutional Variables","[{\"question\":\"What problem does the dissertation address?\",\"answer\":\"It addresses the need to predict which students are at risk of D/Fail/Withdraw (DFW) grades so institutions can support timely graduation, particularly amid declining STEM retention.\"},{\"question\":\"Which factors were found to correlate most with course grades?\",\"answer\":\"Cumulative college GPA and grades from the first four homework assignments showed the highest correlation to course grade outcomes.\"},{\"question\":\"How accurate is early prediction using machine learning?\",\"answer\":\"The study reports grade prediction accuracy of about 80% to 97% within the first two to four weeks of the semester using logistic regression, decision trees, and random forests.\"}]","Using Machine Learning to Predict Student Outcomes | 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