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Predicting academic performance and identifying At-Risk students using machine learning in higher education

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dc.contributor.author Arshad, Sara
dc.date.accessioned 2023-06-05T11:33:08Z
dc.date.available 2023-06-05T11:33:08Z
dc.date.issued 2023
dc.identifier.uri http://10.250.8.41:8080/xmlui/handle/123456789/33891
dc.description.abstract The long-term sustainability of Higher Education Institutes (HEIs) requires predicting student At-Risk to provide them with timely intervention and support. Machine Learn ing (ML) techniques can be helpful if we predict their academic performance and identify At-Risk students. In this research, binary classification is performed on a student dataset of four years (2017-2021) of three different disciplines: Bachelor in Computer Science (BSCS), Bachelor in Electrical Engineering (BEEE), and Bachelor in Software Engineer ing (BESE) obtained from SEECS, in National University of Sciences and Technology (NUST), Islamabad, Pakistan. The research focuses on classifying whether the student is At-Risk or not at risk in a semester. The student with a CGPA below 2.5 is con sidered At-Risk. ML algorithms: Support Vector Machine (SVM), Logistic Regression, XgBoost, are used to achieve the target objective to predict At-Risk students. Differ ent factors were involved in affecting the student’s performance such as demographics, pre-university data, and semester-wise data. . . . en_US
dc.description.sponsorship Dr Arham Muslim en_US
dc.language.iso en en_US
dc.publisher School of Electrical Engineering and Computer Sciences (SEECS) NUST en_US
dc.title Predicting academic performance and identifying At-Risk students using machine learning in higher education en_US
dc.type Thesis en_US


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