Toward effective group formation in computer-supported collaborative learning

Abstract

Group formation task as a starting point for computer-supported collaborative learning plays a key role in achieving pedagogical goals. Various approaches have been reported in the literature to address this problem, but none have offered an optimal solution. In this research, an online learning environment was modeled as a weighted undirected complete graph in which each learner was implied as a node and the relationship between them was denoted as a weighted arc. The weight of each link indicated the similarity degree between the corresponding individuals. The similarity between two students was measured as the mean of their absolute interest levels. The graph was also represented through a symmetric adjacency matrix. Then, a novel binary integer programming formulation was proposed to model the group formation problem and optimally assign each learner to the most appropriate group. The method was utilized to divide an online class of 32 learners into 8 groups of size 4. Findings indicated that the suggested model was successful in optimally solving the problem in 20.53 seconds, on average. The performance of the method was also compared with a modified version of K-means clustering algorithm. Although, the running time of the suggested technique was not as good as the clustering algorithm, it generated better outcomes in theory and in practice.

Keywords:

• computer-supported collaborative learning
• cooperative learning
• group formation
• binary integer programming
• graph-based modeling

Notes on contributors

Hamid Sadeghi is a PhD candidate in IT engineering at Amirkabir University of Technology, Tehran, Iran. He is also a faculty member of the Department of Computer Engineering at Hashtgerd Branch, Islamic Azad University, Alborz, Iran. His main research interests include E-Learning technologies, computer-supported collaborative learning (CSCL), optimization methods, data mining, Web information retrieval, metasearch engine designing, search engines and metasearch engines performance evaluation, web search technology and aggregation operators. He has published in high-quality international journals such as Computers & Education, International Journal of Computational Intelligence Systems, RAIRO-Operations Research and Online Information Review and International Journal of Intelligent Systems. Hamid Sadeghi can be contacted at: [email protected]

Ahmad A. Kardan received his BS in Electrical Engineering from Sharif University of Technology (1976-Iran), his M.Sc. in Digital Systems from the Brunel University (1997-UK), and his PhD in Bio-Electric Engineering from Imperial College of Science and Technology (2001-UK). He founded E-Learning Center of Amirkabir University of Technology in 2000 and managed the center till 2004. He is currently a faculty member of the Computer Engineering Department, at Amirkabir University of Technology, Tehran, Iran. He teaches graduate courses in computing and information technology with emphasis on advanced e-learning and distributed educational systems. Dr Kardan is involved in researches in Intelligent Tutoring Systems (ITS), Learning Advisory Systems, Adaptive Learning, Learner Modeling, Concept Mapping, Collaborative Learning, Annotation Processing and Applying Data Discovery in e-Learning. He has presented more than 80 papers at national and international conferences, journals and as chapters for related books. Dr Kardan can be contacted at: [email protected]