Peter W. Waiganjo

Work place: School of Computing and Informatics University of Nairobi, P. O. Box 30197, 00100, Nairobi, Kenya



Research Interests: Bioinformatics, Artificial Intelligence


Prof. Peter Waiganjo Wagacha is a faculty member at the School of Computing & Informatics, University of Nairobi. He received Bsc. Science (Hons) degree from University of Nairobi in 1991, Msc. in Computer science and Applications from Shanghai University, China in 1996 and PhD in Computer Science from University of Nairobi, Kenya in 2003. He enjoys teaching, research and working with students to develop innovative ideas and solutions. His research and extension work in ICT4D is in the areas of (1) enhancing ICT in education, such as e-learning using Artificial Intelligence, (2) mobility and urban transportation (3) human language technology for local languages (4) health informatics (5) mobile technology. He has published in refereed journals, book chapters, and conference proceedings.

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

By Elizaphan M. Maina Robert O. Oboko Peter W. Waiganjo

DOI:, Pub. Date: 8 Mar. 2017

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.

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