Abstract
Heterogeneity in learning groups is said to improve academic performance. But only few collaborative online systems consider the formation of heterogeneous groups. In this paper we propose a mathematical approach to form heterogeneous groups based on personality traits and the performance of students. We also present a tool that implements this mathematical approach, using an Ant Colony Optimization algorithm in order to maximize the heterogeneity of formed groups. Experiments show that the algorithm delivers stable solutions which are close to the optimum for different datasets of 100 students. An experiment with 512 students was also performed demonstrating the scalability of the algorithm.
This research has been funded partly by the Austrian Federal Ministry for Education, Science, and Culture, and the European Social Fund (ESF) under grant 31.963/ 46-VII/9/2002.
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Graf, S., Bekele, R. (2006). Forming Heterogeneous Groups for Intelligent Collaborative Learning Systems with Ant Colony Optimization. In: Ikeda, M., Ashley, K.D., Chan, TW. (eds) Intelligent Tutoring Systems. ITS 2006. Lecture Notes in Computer Science, vol 4053. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11774303_22
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DOI: https://doi.org/10.1007/11774303_22
Publisher Name: Springer, Berlin, Heidelberg
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