You are here:

A genetic algorithm approach for group formation in collaborative learning considering multiple student characteristics
ARTICLE

, ,

Computers & Education Volume 58, Number 1, ISSN 0360-1315 Publisher: Elsevier Ltd

Abstract

Considering that group formation is one of the key processes in collaborative learning, the aim of this paper is to propose a method based on a genetic algorithm approach for achieving inter-homogeneous and intra-heterogeneous groups. The main feature of such a method is that it allows for the consideration of as many student characteristics as may be desired, translating the grouping problem into one of multi-objective optimization. In order to validate our approach, an experiment was designed with 135 college freshmen considering three characteristics: an estimate of student knowledge levels, an estimate of student communicative skills, and an estimate of student leadership skills. Results of such an experiment allowed for the validation, not only from the computational point of view by measuring the algorithmic performance, but also from the pedagogical point of view by measuring student outcomes, and comparing them with two traditional group formation strategies: random and self-organized.

Citation

Moreno, J., Ovalle, D.A. & Vicari, R.M. (2012). A genetic algorithm approach for group formation in collaborative learning considering multiple student characteristics. Computers & Education, 58(1), 560-569. Elsevier Ltd. Retrieved October 20, 2019 from .

This record was imported from Computers & Education on January 29, 2019. Computers & Education is a publication of Elsevier.

Full text is availabe on Science Direct: http://dx.doi.org/10.1016/j.compedu.2011.09.011

Keywords

Cited By

View References & Citations Map

These links are based on references which have been extracted automatically and may have some errors. If you see a mistake, please contact info@learntechlib.org.