A METHODOLOGY FOR FORMING EFFECTIVE STUDENT TEAMS IN ENGINEERING ‎CLASSES

S. Salhieh, G. Cormier

Alfaisal University (SAUDI ARABIA)
The complex nature of engineering systems mandates that engineers work within interdisciplinary work ‎teams ‎capable of sharing knowledge and expertise. These teams should possess all the necessary technical ‎background ‎needed to solve the problem. In addition, team formation requires the consideration of the ‎interpersonal skills that ‎has a profound impact on the success or failure of teams.

Training engineers to work within teams is considered a major concern in engineering education. Engineering ‎teams ‎could range from pure functional teams focusing on a single technical task to cross-functional teams where ‎an ‎engineer needs to interact with team members from other disciplines. This requires careful selection of ‎team ‎members such that a team can achieve its goal. Forming an “ideal” team that possesses all the correct ‎interpersonal ‎characteristics is considered a challenging task due to the difficulty and ambiguity in assessing team ‎members’ ‎personality types and determining the personality types that best fit the task assigned to the teams. ‎

This paper proposes using the Myers Briggs Type Indicator (MBTI) as a basis for assessing the personality types ‎of ‎team candidates. The paper provides a systematic approach that utilizes QFD as the overall structure that ‎captures ‎all the information needed about the task assigned to the team including the technical and ‎interpersonal ‎requirements needed to complete the task. Also, the QFD matrix is used to capture all the information ‎about the ‎performance of the candidates with respect to the technical and interpersonal requirements needed. Then, ‎all the ‎information available in the QFD are used in an integer programming model to select the candidates to be ‎included ‎in the team.‎

The developed approach was tested by conducting an experiment using students enrolled in the ‎‎“Product ‎Development” course. The results of the experiment showed that the proposed approach has the ability to ‎predict ‎the performance of the teams and can be used to form effective engineering teams.‎