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1. Relationship Between Team-Building Activities and Capstone Team Performance and Student Experience

   https://doi.org/10.18260/1-2--46949  

   Godbole, Hrushikesh; DeBartolo, Elizabeth; Takai, Shun (June 2024, ASEE Conferences)

   Team building activities are popular interventions during early stages of team development. At RIT, in the multidisciplinary capstone course with an average cohort size of around 350, the students on a particular capstone project team may not be mutually acquainted and thus may benefit from such team building activities. Prior literature has studied the effectiveness of various instructor-directed team building activities on student teams. However, our students are generally eager to spend class time working on their projects and often see in-class activities as a distraction rather than an important part of their growth. Instead, the student teams are now allowed to choose an intervention based on team consensus. In this paper, the relationship between attributes of the chosen intervention and student performance, as measured using a series of AACU VALUE rubrics, was studied using statistical measures. The analysis revealed a statistically significant effect of type of team building activity on teamwork, oral communication, and design & problem solving scores of individual students on the team. Also, a statistically significant effect of location of team building activity (on or off campus) on design & problem solving score was observed. 

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2. Using Explainable AI to Understand Team Formation and Team Impact

   https://doi.org/10.1002/pra2.804  

   Xu, Huimin; Saar‐Tsechansky, Maytal; Song, Min; Ding, Ying (October 2023, Proceedings of the Association for Information Science and Technology)

   ABSTRACT The citation of scientific papers is considered a simple and direct indicator of papers' impact. This paper predicts papers' citations through team‐related variables, team composition, and team structure. Team composition includes team size, male/female dominance, academia/industry collaboration, unique race number, and unique country number. Team structures are made up of team power level and team power hierarchy. Team members' previous citation number, H‐index, previous collaborators, career age, and previous paper numbers are a proxy of team power. We calculated the mean value and Gini coefficient to represent team power level (the collective team capability) and team power hierarchy (the vertical difference of power distribution within a team). Taking 1,675,035 CS teams in the DBLP dataset, we trained the XGBoost model to predict high/low citation. Our model has reached 0.71 in AUC and 70.45% in accuracy rate. Utilizing Explainable AI method SHAP to evaluate features' relative importance in predicting team citation categories, we found that team structure plays a more critical role than team composition in predicting team citation. High team power level, flat team power structure, diverse race background, large team, collaboration with industry, and male‐dominated teams can bring higher team citations. Our project can provide insights into how to form the best scientific teams and maximize team impact from team composition and team structure. 

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3. Board 366: Relationship Between Team-Building Activities and Capstone Team Performance and Student Experience

   Godbole, H; DeBartolo, E; Takai, S (June 2024, Proceeding of the 2024 ASEE Annual Conference and Exposition)
4. Developing, Sustaining, and Maximizing Team Effectiveness: An Integrative, Dynamic Perspective of Team Development Interventions

   https://doi.org/10.5465/annals.2016.0045  

   Shuffler, Marissa L.; Diazgranados, Deborah; Maynard, M. Travis; Salas, Eduardo (June 2018, Academy of Management Annals)
5. Common Knowledge and Sequential Team Problems

   https://doi.org/10.1109/TAC.2019.2912536  

   Nayyar, Ashutosh; Teneketzis, Demosthenis (December 2019, IEEE Transactions on Automatic Control)