Original Research

Agent-based adaptive e-learning model for any learning management system

George Wamamu Musumba, Robert O. Oboko, Henry O. Nyongesa
International Journal of Machine Learning and Applications | Vol 2, No 1 | a6 | DOI: https://doi.org/10.4102/ijmla.v2i1.6 | © 2013 George Wamamu Musumba, Robert O. Oboko, Henry O. Nyongesa | This work is licensed under CC Attribution 4.0
Submitted: 29 January 2013 | Published: 06 June 2013

About the author(s)

George Wamamu Musumba, Dedan Kimathi University of Technology, Kenya
Robert O. Oboko, School of Computing and Informatics, University of Nairobi, Kenya
Henry O. Nyongesa, Department of Computer Science, University of the Western Cape, South Africa


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Abstract

Many scholars are interested in improving e-learning in order to provide easy access to educational materials. There is, however, the need to incorporate the ability to classify learners into these learning systems. Learner classification is used adaptively to provide relevant information for the various categories of learners. There is also a need for learning to continue, whether learners are on- or off-line. In many parts of the world, especially in the developing world, most people do not have reliable continuous internet connections. We tested an Adaptive e-Learning Model prototype that implements an adaptive presentation of course content under conditions of intermittent Internet connections. This prototype was tested in February 2011 on undergraduate students studying a database systems course. This study found out that it is possible to have models that can adapt to characteristics such as the learner’s level of knowledge and that it is possible for learners to be able to study under both  on- and off-line modes through adaptation.


Keywords

Application programming interface (API), Dynamic link library (DLL), K-nearest neighbor (KNN), Intermittent internet connections, Learner model

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