Using Machine Learning Techniques to Support Group Formation in an Online Collaborative Learning Environment
Автор: Elizaphan M. Maina, Robert O. Oboko, Peter W. Waiganjo
Журнал: International Journal of Intelligent Systems and Applications(IJISA) @ijisa
Статья в выпуске: 3 vol.9, 2017 года.
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The current Learning Management Systems used in e-learning lack intelligent mechanisms which can be used by an instructor to group learners during an online group task based on the learners' collaboration competence level. In this paper, we discuss a novel approach for grouping students in an online learning group task based on individual learners' collaboration competence level. We demonstrate how it can be applied in a Learning Management System such as Moodle using forum data. To create the collaboration competence levels, two machine learning algorithms for clustering namely Skmeans and Expectation Maximization (EM) were applied to cluster data and generate clusters based on learner's collaboration competence. We develop an intelligent grouping algorithm which utilizes these machine learning generated clusters to form heterogeneous groups. These groups are automatically made available to the instructor who can proceed to assign them to group tasks. This approach has the advantage of dynamically changing the group membership based on learners' collaboration competence level.
Learning Management Systems, Online Group Task, Collaboration Competence Level, Intelligent grouping Algorithm, Machine learning
Короткий адрес: https://sciup.org/15010910
IDR: 15010910
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