Robert O. Oboko

Work place: University of Nairobi, School of Computing & Informatics, Kenya



Research Interests: Computational Learning Theory


Robert O. Oboko received his Msc. Computer science from Free University of Brussels and PhD in Computer Science from University of Nairobi.

He is a Lecturer in the School of Computing and Informatics in the University of Nairobi, Kenya. His research interests are mainly in issues around the use of ICT for Development. These include issues around application of ICT in Education, Monitoring and Evaluation, health, enhancement of Social Capital, and ICT4D policy research, among others. He is also keen on the use of machine learning and mobile devices for development.

Dr. Oboko regularly publishes international refereed journal papers, refereed international conferences papers and book chapters.

Author Articles
Use of Intelligent Agents in Collaborative M-Learning: Case of Facilitating Group Learner Interactions

By Stephen T. Njenga Robert O. Oboko Elijah I. Omwenga Elizaphan M. Maina

DOI:, Pub. Date: 8 Oct. 2017

Intelligent agents have been used in collaborative learning. However, they are rarely used to facilitate group interactions in collaborative m-learning environments. In view of this, the paper discusses the use of intelligent agents in facilitating collaborative learning in mobile learning environments. The paper demonstrates how to design intelligent agents and integrate them in collaborative mobile learning environments to allow group learners to improve their levels of group knowledge construction. The design was implemented in a collaborative mobile learning system running on Modular Object-Oriented Dynamic Learning Environment (Moodle) platform. The application was used in some experiments to investigate the effects of those facilitated interactions on the level of group knowledge construction. The results showed improved levels of group knowledge construction in instances where the facilitations were enabled compared to where they were disabled. The paper concludes that the use of intelligent agents in facilitating learner group interactions in collaborative mobile learning environments improves the levels of group knowledge construction. For future work, the use of intelligent agents can be tested in other areas of group interactions to enhance group learning.

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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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Other Articles