An Enhanced Genetic Approach to Composing Cooperative Learning Groups for Multiple Grouping Criteria

ABSTRACT Cooperative learning is known to be an effective educational strategy in enhancing the learning performance of students. The goal of a cooperative learning group is to maximize all members' learning efficacy. This is accomplished via promoting each other's success, through assisting, sharing, mentoring, explaining, and encouragement. To achieve the goal of cooperative learning, it is very important to organize well-structured cooperative learning groups. In this study, an enhanced genetic algorithm is proposed to organize cooperative learning groups to meet multiple grouping criteria. To show the usefulness of the algorithm, this study presents a case that, for a given course, the teacher sets the criteria of grouping that each concept of a certain course topic is precisely understood by at least one of the peer students in each group, and the average learning achievement of each group is approximately identical. Based on our enhanced genetic algorithm, an assistant system for organizing cooperative learning groups has been developed. Experimental results have shown that the enhanced approach is able to efficiently organize cooperative learning groups that more fit the instructional objectives set by the instructor.