Group Formation Using Multi Objectives Ant Colony System for Collaborative Learning

Abstract

Collaborative learning is widely applied in education. One of the key aspects of collaborative learning is group formation. A challenge in group formation is to determine appropriate attributes and attribute types to gain good group results. This paper studies the use of an improved ant colony system (ACS), called Multi Objective Ant Colony System (MOACS), for group formation. Unlike ACS that transforms all attribute values into a single value, thus making any attributes are not optimally worth, MOACS tries to gain optimal values of all attributes simultaneously. MOACS is designed for various combinations of attributes and can be used for homogeneous, heterogeneous or mixed attributes. In this paper, sensing/intuitive learning styles (LSSI) and interests in subjects (I) are used in homogeneous group formation, while active/reflective learning style (LSAR) and previous knowledge (KL) are used for heterogeneous or mixed group formation. Experiments were conducted for measuring the average goodness of attributes (avgGA) and standard deviation of goodness of attributes (stdGA). The objectives of MOACS for homogeneous attributes were minimum avgGA and stdGA, while those for heterogeneous attributes were maximum avgGA and minimum stdGA. As a conclusion, MOACS was appropriate for group formation with homogeneous or mixed.

Formasi kelompok dinamis untuk mendukung kolaborasi pembelajaran proyek perangkat lunak

“…Algoritma genetika (Lambora, Gupta, & Chopra, 2019); 2.) Ant colony (Fahmi & Nurjanah, 2018); dan 3.) Discrete PSO (Zheng & Pinkwart, 2014).…”

Group formation in collaborative learning contexts based on personality traits: An empirical study in initial Programming courses

“…Specifically, homogeneous grouping presents certain advantages for some types of learning activities, especially those that involve guided discovery, development of skills, review of material that has already been learned, or in highly structured tasks of competencies construction, allowing students to progress at a similar rate, which is beneficial for the achievement of specific goals [1][2][3][4]; this grouping type promotes a positive effect on collaborative learning [5][6][7]. Such is the case of activities that support learning processes in initial programming courses, the context of this study; where, also, considering the personality traits of students in the group formation process enhances their collaborative performance, when it comes to software development activities [8][9][10][11][12][13][14][15][16][17] This paper presents the results of the research process carried out to structure a homogeneous group formation technique in collaborative learning contexts, formation based on personality traits.…”

Forming automatic groups of learners using particle swarm optimization for applications of differentiated instruction

“…These studies treat the formation of homogeneous or heterogeneous groups as a complex problem. In this context, Ani et al , Pinninghoff et al , Moreno, Ovalle and Vicari , Wang et al , Hwang et al , Chan et al , and Zheng et al use genetic algorithms (GA), Takači et al and Lambić et al use variable neighborhood search algorithm, Ηο et al , Lin et al and Ullmann et al use swarm intelligence optimization algorithms and Graf and Bekele as well as Fahmi et al use ant colony optimization, to form student groups based on a variety of characteristics. Finally, Zervoudakis, Mastrothanasis and Kalovrektis use a differential evolution algorithm for automatic group formation.…”