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Computer Science and Education in Computer Science. 19th EAI International Conference, CSECS 2023, Boston, MA, USA, June 28–29, 2023, Proceedings

Research Article

TeamUp: Form Best Project Teams

Cite
BibTeX Plain Text
  • @INPROCEEDINGS{10.1007/978-3-031-44668-9_29,
        author={Dawei Yin and Sha Hu and Yuting Zhang},
        title={TeamUp: Form Best Project Teams},
        proceedings={Computer Science and Education in Computer Science. 19th EAI International Conference, CSECS 2023, Boston, MA, USA, June 28--29, 2023, Proceedings},
        proceedings_a={CSECS},
        year={2023},
        month={10},
        keywords={team formation team project project-based learning},
        doi={10.1007/978-3-031-44668-9_29}
    }
    
  • Dawei Yin
    Sha Hu
    Yuting Zhang
    Year: 2023
    TeamUp: Form Best Project Teams
    CSECS
    Springer
    DOI: 10.1007/978-3-031-44668-9_29
Dawei Yin1, Sha Hu1, Yuting Zhang1,*
  • 1: Boston University, Boston
*Contact email: danazh@bu.edu

Abstract

Team formation is a critical task in many contexts such as business, sports, healthcare, research, education, and more. In the academic settings, students working in teams on group projects is proven to be a very effective learning methodology. Our software engineering course features a semester-long team project as the key component of this course. However, team assignments are complex and nontrivial tasks, necessitating a careful assessment of many different factors to ensure optimal performance of the team as well as individual participants’ satisfaction. Usually a predefined set of questions are used to understand participant’s capabilities and preferences, and teams are formed either manually or automatically based on the results of those questions. In this paper, we propose a generalized question definition by associating each question with three factors: multiple choice/multiple answer, similarity or diversity, and option valuation, in order to consider various types of factors, provide flexibility and capture each individual’s characteristics. We then propose two team performance score functions that differentiate similarity questions from diversity questions and capture each of their team formation objectives. A heuristic team formation algorithm, TeamUp, is proposed, attempting to maximize the team performance as well as participants’ preferences. Through the initial evaluation we show our proposed algorithm can perform well for different team size and type of questions.

Keywords
team formation team project project-based learning
Published
2023-10-11
Appears in
SpringerLink
http://dx.doi.org/10.1007/978-3-031-44668-9_29
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