Student teams’ development over time: tracing the relationship between the quality of communication and teams’ performance

Abstract

Current discussions in higher education and alumni training acknowledge the challenges training programs face in responding to the authentic needs of the labor market. In addition to academic knowledge, higher education institutions are expected to provide general twenty-first-century skills, such as problem-solving, critical thinking, collaboration, and interpersonal skills. To meet these challenges, many institutions utilize collaborative pedagogies such as learning in teams. However, teamwork in higher education tends to focus primarily on the task aspects of performance at the expense of the team aspects, and for educators, there may be no feasible way to assess whether the students are learning to work successfully as teams. This paper explores how new student teams (n = 3) that simulate real business teams by taking a challenging entrepreneur assessment, developed over three semesters for general skills (i.e., communication), and whether the improvement in their communication also indicated the teams’ improved performance (i.e., financial success). As an analytical tool, the study relies on initial parameters on teams’ microdynamics of communication [Losada, M. (1999). The complex dynamics of high performance teams. Mathematical and Computer Modelling, 30, 179–192] normalized with fuzzy logic. In accordance with the current understanding of team development, the results did not show any linear improvement, but the quality of communication in the teams improved episodically. Further, the results provide evidence of the possible relationship between the improved quality of communication and the teams’ collective financial success. However, in future work, due to the lack of sensitivity of the parameters in this context together with the recent criticisms of the mathematical basis of the patterns of team dynamics based on Losada's parameters, they will be reexamined with a Monte Carlo sensitivity analysis.