Using Machine Learning Techniques to Enhance Adaptive Learning Management System in The Case of Kenyan Universities

Ms. Lucy A. Abuodha, Mr. Martin K. Busi and Dr. Nelson Masese
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Ms. Lucy A. Abuodha: School of Computing and Information Technology, Computer Science, Technical University of Kenya, Box 52428 – 00200 Nairobi, Kenya.
Mr. Martin K. Busi: School of Science Engineering and Technology, Kabarak University, Box: 20157 Nakuru, Kenya
Dr. Nelson Masese: School of Science Engineering and Technology, Kabarak University, Box: 20157 Nakuru, Kenya

International Journal of Research and Innovation in Social Science, 2024, vol. 8, issue 7, 385-391

Abstract: Universities in Kenya have a substantial increase in the use of learning management systems (LMSs) to support e-learning. These universities have adopted LMS to ensure that students are widely reached and at the same time experience effective learning. The universities have still not adopted Adaptive LMS to help in this learning approach. Adaptive learning management systems are designed to personalize the learning experience for each individual student, taking into account their unique needs and abilities. In combination with Machine Learning techniques (ML), such technology has unprecedentedly allow students to have adaptive feedback, adaptive assessment as well as adaptive learning content.This paper proposes on how the use of machine learning techniques can enhance adaptive LMS to analyze student data and automatically adjust the learning experience accordingly. The paper recommends that Kenyan universities should adopt use of machine learning techniques to significantly improve the effectiveness of adaptive learning management systems, leading to better student outcomes and a more efficient learning.

Date: 2024
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