Critical Literature Review on Current State-of-the Art in Predicting Students’ Performance using Machine Learning Algorithm in Blended Learning Environment

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dc.contributor.authorOfori, Francis
dc.contributor.authorMatheka, Abraham
dc.contributor.authorMaina, Elizaphan
dc.date.accessioned2023-09-04T13:12:06Z
dc.date.available2023-09-04T13:12:06Z
dc.date.issued2023
dc.descriptionArticleen_US
dc.description.abstractBackground 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%.en_US
dc.identifier.citationOfori, 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/465en_US
dc.identifier.urihttps://ajoeijournals.org/sys/index.php/ajoei/article/view/465
dc.identifier.urihttp://ir-library.ku.ac.ke/handle/123456789/26885
dc.language.isoenen_US
dc.publisherAJOEIen_US
dc.subjectStudents’ Performanceen_US
dc.subjectMachine Learning Algorithmen_US
dc.subjectDatasetsen_US
dc.subjectMoodleen_US
dc.subjectLMSen_US
dc.subjectBlended Learningen_US
dc.titleCritical Literature Review on Current State-of-the Art in Predicting Students’ Performance using Machine Learning Algorithm in Blended Learning Environmenten_US
dc.typeArticleen_US
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