Using personality inventories to help form teams for software engineering class projects
Proceedings of the 6th annual conference on Innovation and technology in computer science education
The effects of roles and personality characteristics on software development team effectiveness
The effects of roles and personality characteristics on software development team effectiveness
The impact of learning styles on student grouping for collaborative learning: a case study
User Modeling and User-Adapted Interaction
OptAssign-A web-based tool for assigning students to groups
Computers & Education
Studying the impact of personality and group formation on learner performance
CRIWG'07 Proceedings of the 13th international conference on Groupware: design implementation, and use
Hi-index | 12.05 |
The aim of forming collaborative learning teams is that participating students acquire new knowledge and skills through the interaction with their peers. To reach this aim, teachers usually utilize a grouping criterion based on the students' roles and on forming well-balanced teams according to the roles of their members. However, the implementation of this criterion requires a considerable amount of time, effort and knowledge on the part of the teachers. In this paper, we propose a deterministic crowding evolutionary algorithm with the aim of assisting teachers when forming well-balanced collaborative learning teams. Considering a given number of students who must be divided into a given number of teams, the algorithm both designs different alternatives to divide students into teams and evaluates each alternative as regards the grouping criterion previously mentioned. This evaluation is carried out on the basis of knowledge of the students' roles. To analyze the performance of the proposed algorithm, we present the computational experiments developed on ten data sets with different levels of complexity. The obtained results are really promising since the algorithm has reached optimal solutions for the first four data sets and near-optimal solutions for the remaining six data sets.