Machine Learning Utilization to Predict Potential At-Risk Students in Higher Education: A Literature Review
Keywords:
Machine Learning, Predictive Models, Higher Education, Theoretical Models, Literature Review, Artificial IntelligenceAbstract
A prosperous and functional society always requires capable and qualified human resources to operate. Ensuring its continuity demands the success of education as the foundation for fostering and nurturing future generations and enhancing human capacity. This study aims to provide insights into various state-of-the-art predictive models to improve student retention in higher education. This study examined 37 reputable papers selected through systematic literature review techniques and discussed 7 theoretical models, 7 relational models, and 14 predictive models. The best predictive models were able to attain 93.0% accuracy using linear regression and random forest algorithms. The study found that the development of predictive machine learning requires thorough planning and preparation suited to each educational institution, and its utilization is highly recommended to enhance student retention in higher education.
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