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Predicting academic success has gained interest in education as a strong academic record improves a university\u2019s ranking and increases student employment opportunities. Modern learning institutions face challenges in analyzing performance, providing high\u2010quality education, formulating strategies for evaluating students\u2019 performance, and identifying future needs. E\u2010learning is a rapidly growing and advanced form of education, where students enroll in online courses. Platforms like Intelligent Tutoring Systems (ITS), learning management systems (LMS), and massive open online courses (MOOC) use educational data mining (EDM) to develop automatic grading systems, recommenders, and adaptative systems. However, e\u2010learning is still considered a challenging learning environment due to the lack of direct interaction between students and course instructors. Machine learning (ML) is used in developing adaptive intelligent systems that can perform complex tasks beyond human abilities. Some areas of applications of ML algorithms include cluster analysis, pattern recognition, image processing, natural language processing, and medical diagnostics. In this research work, K\u2010means, a clustering data mining technique using Davies\u2019 Bouldin method, obtains clusters to find important features affecting students\u2019 performance. The study found that the SVM algorithm had the best prediction results after parameter adjustment, with a 96% accuracy rate. In this paper, the researchers have examined the functions of the Support Vector Machine, Decision Tree, naive Bayes, and KNN classifiers. The outcomes of parameter adjustment greatly increased the accuracy of the four prediction models. Na\u00efve Bayes model\u2019s prediction accuracy is the lowest when compared to other prediction methods, as it assumes a strong independent relationship between features.<\/jats:p>","DOI":"10.1155\/2024\/4067721","type":"journal-article","created":{"date-parts":[[2024,5,1]],"date-time":"2024-05-01T00:20:10Z","timestamp":1714522810000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":76,"title":["Student Performance Prediction Using Machine Learning Algorithms"],"prefix":"10.1155","volume":"2024","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8144-1315","authenticated-orcid":false,"given":"Esmael","family":"Ahmed","sequence":"first","affiliation":[]}],"member":"311","published-online":{"date-parts":[[2024,4,30]]},"reference":[{"key":"e_1_2_10_1_2","doi-asserted-by":"crossref","unstructured":"BaasharY. AlkawsiG. AliN. AlhussianH. andBahbouhH. T. 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