Using Machine Learning Techniques to Support Group Formation in an Online Collaborative Learning Environment

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dc.contributor.authorMaina, Elizaphan M.
dc.contributor.authorOboko, Robert O.
dc.contributor.authorWaiganjo, Peter W.
dc.date.accessioned2017-12-05T12:15:39Z
dc.date.available2017-12-05T12:15:39Z
dc.date.issued2017
dc.descriptionResearch Articleen_US
dc.description.abstractThe 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.en_US
dc.identifier.citationI.J. Intelligent Systems and Applications Vol. 3 pp 26-33 2017en_US
dc.identifier.urihttp://ir-library.ku.ac.ke/handle/123456789/17901
dc.language.isoenen_US
dc.publisherModern Education Computer Science Pressen_US
dc.titleUsing Machine Learning Techniques to Support Group Formation in an Online Collaborative Learning Environmenten_US
dc.typeArticleen_US
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