Ofori, FrancisMatheka, AbrahamMaina, Elizaphan2023-09-042023-09-042023Ofori, F., Matheka, A., & Maina, E. (2023). CRITICAL LITERATURE REVIEW ON CURRENT STATE-OF-THE ART IN PREDICTING STUDENTS’ PERFORMANCE USING MACHINE LEARNING ALGORITHM IN BLENDED LEARNING ENVIRONMENT. African Journal of Emerging Issues, 5(12), 23 - 38. Retrieved from https://ajoeijournals.org/sys/index.php/ajoei/article/view/465https://ajoeijournals.org/sys/index.php/ajoei/article/view/465http://ir-library.ku.ac.ke/handle/123456789/26885ArticleBackground of the study: Predicting and analyzing the performance of the student in a blended learning environment is important to help educators identify poor performing students and improve their academic score. Meanwhile, achieving accurate predictions require selecting machine learning techniques that can produce optimum score. However, there seems to be no critical literature review on current state of art in predicting students’ performance using machine learning algorithms in blended learning environment. Methodology: This critical literature review focuses on, studies on the current state of the art in predicting students’ performance in the blended learning for past 10 years, sources of dataset used by various authors and the machined learning algorithm with high prediction accuracy. Findings: Naïve Bayes was the most frequently used algorithm for predicting students’ performance. Authors mostly used online data for their student’s performance prediction. Finally, artificial neural network was found to give higher prediction accuracy of 98.7%.enStudents’ PerformanceMachine Learning AlgorithmDatasetsMoodleLMSBlended LearningArticle