A multi-objective optimization approach for the group formation problem

Abstract Group formation is one of the essential stages of collaborative learning. This paper proposes an intelligent computational approach to optimize the group formation process taking into account multiple criteria: inter-homogeneity, intra-heterogeneity, and empathy. More specifically, it uses a genetic algorithm to maximize the number of different student profiles in each group. The proposed method was evaluated regarding its computational performance comparing against three baselines; and in a real educational application, where it was compared with random and self-organized methods. The results showed the potential of the proposed method from both the computational and pedagogical points of view.

[1]  Rahul Roy,et al.  A Combinatorial Multi-objective Particle Swarm Optimization Based Algorithm for Task Allocation in Distributed Computing Systems , 2011, ACC.

[2]  Silje Alberthe Kamille Friis,et al.  Including diversity in creative teamwork in design education , 2015 .

[3]  Kalyanmoy Deb,et al.  Multi-objective Optimization , 2014 .

[4]  P. Hall,et al.  Interdisciplinary education and teamwork: a long and winding road , 2001, Medical education.

[5]  Anastasios A. Economides,et al.  Learning Analytics and Educational Data Mining in Practice: A Systematic Literature Review of Empirical Evidence , 2014, J. Educ. Technol. Soc..

[6]  Minou Ella Mebane,et al.  Developing professional skills and social capital through computer supported collaborative learning in university contexts , 2007, Int. J. Hum. Comput. Stud..

[7]  Demetrio Arturo Ovalle Carranza,et al.  A genetic algorithm approach for group formation in collaborative learning considering multiple student characteristics , 2012, Comput. Educ..

[8]  Natalie Simper,et al.  Assessing teamwork in undergraduate education: a measurement tool to evaluate individual teamwork skills , 2017 .

[9]  Yannis A. Pollalis,et al.  Using similarity measures for collaborating groups formation: A model for distance learning environments , 2009, Eur. J. Oper. Res..

[10]  Jane E. Klobas,et al.  Evaluation of the efficacy of collaborative learning in face-to-face and computer-supported university contexts , 2006, Comput. Hum. Behav..

[11]  Chen-Chung Liu,et al.  An analysis of peer interaction patterns as discoursed by on-line small group problem-solving activity , 2008, Comput. Educ..

[12]  Weigang Lu,et al.  An improved genetic approach for composing optimal collaborative learning groups , 2018, Knowl. Based Syst..

[13]  R. Land,et al.  Assigning Students in Group Work Projects. Can We Do Better than Random? , 2000 .

[14]  Miltiadis D. Lytras,et al.  Improving e-learning communities through optimal composition of multidisciplinary learning groups , 2014, Comput. Hum. Behav..

[15]  Annemette Kjærgaard,et al.  Student team formation based on learning styles at university start: Does it make a difference to the students? , 2009 .

[16]  Saskia Brand-Gruwel,et al.  Students' experiences with collaborative learning in asynchronous Computer-Supported Collaborative Learning environments , 2004, Comput. Hum. Behav..

[17]  Yen-Ting Lin,et al.  An automatic group composition system for composing collaborative learning groups using enhanced particle swarm optimization , 2010, Comput. Educ..

[18]  Rafael Bravo,et al.  Analysing teamwork in higher education: an empirical study on the antecedents and consequences of team cohesiveness , 2019 .

[19]  Zhamri Che Ani,et al.  A Method for Group Formation Using Genetic Algorithm , 2010 .

[20]  F. J. Zarazaga-Soria,et al.  Deconstructing the Tower of Babel: a design method to improve empathy and teamwork competences of informatics students , 2017 .

[21]  Kalyanmoy Deb,et al.  A fast and elitist multiobjective genetic algorithm: NSGA-II , 2002, IEEE Trans. Evol. Comput..

[22]  Daniel A. Keim,et al.  Guiding the exploration of scatter plot data using motif-based interest measures , 2016, J. Vis. Lang. Comput..

[23]  A Ricardo Contreras,et al.  Genetic algorithms as a tool for structuring collaborative groups , 2017 .

[24]  Witold Pedrycz,et al.  Computational Intelligence: An Introduction , 1997, Computational Intelligence and Quantitative Software Engineering.

[25]  Jun Shen,et al.  Optimal learning group formation: A multi-objective heuristic search strategy for enhancing inter-group homogeneity and intra-group heterogeneity , 2019, Expert Syst. Appl..

[26]  C. Crook.  Computers and the Collaborative Experience of Learning , 1996 .

[27]  Sabine Graf,et al.  Forming Heterogeneous Groups for Intelligent Collaborative Learning Systems with Ant Colony Optimization , 2006, Intelligent Tutoring Systems.

[28]  Mohammad Teshnehlab,et al.  Evolutionary dynamic multi-objective optimization algorithm based on Borda count method , 2017, International Journal of Machine Learning and Cybernetics.

[29]  Dragan Gasevic,et al.  Learning analytics should not promote one size fits all: The effects of instructional conditions in predicting academic success , 2016, Internet High. Educ..