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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Date
2023
Authors
Ofori, Francis
Matheka, Abraham
Maina, Elizaphan
Journal Title
Journal ISSN
Volume Title
Publisher
AJOEI
Abstract
Background 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%.
Description
Article
Keywords
Students’ Performance, Machine Learning Algorithm, Datasets, Moodle, LMS, Blended Learning
Citation
Ofori, 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/465