A Genetic Algorithm Approach for Inter and Intra Homogeneous Grouping Considering Multi-student Characteristics

A M Aseere

doi:10.14569/ijacsa.2020.0111219

Abstract

This paper addresses the problem of group formation in collaborative learning by considering the students’ characteristics. The proposed solution is based on a Genetic Algorithm (GA), which minimizes an objective function that has two main aims. Indeed, the proposed GA’s fitness function helps to achieve two objectives: Fairness in the formation of different groups, resulting in intergroup homogeneity, and a low gap in the levels of students within a group, which corresponds to intragroup homogeneity. Exhaustive experiments were conducted using three different sizes of randomly generated data sets and several crossover operators. Indeed, the order crossover and the crossovers based on random keys representation are experimented. The reported results show that the proposed approach guarantees the efficient grouping of students. In addition, comparisons with existing approaches based on GA confirm the ability of the proposed approach to provide greater intergroup and intragroup homogeneity. In addition, the uniform crossover based on random keys representation ensures better grouping quality than do the other experimented crossover operators.

Highlights

Group formation is a crucial issue in collaborative learning
This paper proposes a method for combining a number of students into groups in the context of collaborative learning
Based on these reported results, we can state that the proposed Genetic Algorithm (GA) with the uniform crossover based on random keys representation ensures better grouping quality than do the other crossover operators

Summary

INTRODUCTION

Group formation is a crucial issue in collaborative learning. At present, group work is becoming increasingly recommended in various disciplines. Different approaches are presented in the literature to answer research questions related to the problem of group formation. Automatic heterogeneous or homogeneous grouping can be performed based on students’ characteristics [5] These characteristics could be academic (grades, tests, self-evaluations, and so on), cognitive (learning styles intelligence types, and so forth), personality traits (such as leadership skills), or other considerations. Various types of characteristics are used for automatic group formation based on different optimization techniques [2], [4]. A specific objective function based on different types of characteristics is proposed for achieving the desired type of grouping. A two-objective fitness function is proposed to achieve group formation with intragroup and intergroup homogeneity.

PROBLEM FORMULATION

GENETIC ALGORITHM DEPLOYMENT

Experimental Protocol

Results Analysis

CONCLUSION AND FUTURE WORK