¹Department of Information Management, Chung Hua University, Taiwan
²Department of Information Management, Chihlee Institute of Technology, Taiwan
³Informatics Department, Taipei Medical University Hospital, Taiwan

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Abstract—Collaborative learning promotes learning motivation which encourages the active participation and leads to good learning performance. In most cases, good grade is usually a good indicator of good learner. Teachers might classify learners into various groups according to their grade. As a result, learners with poor grades are easily depressed and self-distrust. In this paper, we integrate K-means clustering method and IRT forecasting process to solve the grouping issue in collaborative learning. With a better grouping solution, teachers then can adjust the learning materials adaptively and teach students according to the learning aptitude.

Index Terms—Learning ability, Item Response Theory, Kmeans, Collaborative Learning.

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Cite: Wen-Chih Chang, Te-Hua Wang, Mao-Fan Li, "Learning Ability Clustering in Collaborative Learning," Journal of Software vol. 5, no. 12, pp. 1363-1370, 2010.