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Ability to effectively work in teams is one of the desired outcomes of engineering and engineering technology programs. Unfortunately, working in teams is still challenging for many students. Rather than contributing to team projects, some students resort to social loafing. Social loafing tends to destroy both teamwork performance and individual learning, especially in solving ill-structured problems, such as design. Furthermore, a bad experience on a past team is a significant concern as it could generate negative feelings toward future team projects. Formation of collaborative teams is a critical first step in team-project-based design courses as team composition directly affects not only teamwork processes and outcomes, but also teamwork skills and experience. This NSF-IUSE sponsored project aims to enhance students’ teamwork experiences and teamwork learning through 1) understanding how to form better student design teams and 2) identifying exercises that will effectively improve team member collaboration. We do this by comparing student characteristics and design task characteristics with the quality of the design team outcome, and examining the resulting correlations. Student characteristics cover six categories: 1) background information, 2) work structure preferences, 3) personality, 4) ability, 5) motivation, and 6) attitude. Task characteristics and design team outcomes are characterized using the Creative Product Semantic Scale. In this article, we present correlations between student/team characteristics and design project outcome, and correlations between task characteristics and design project outcome for 2020-2021 senior design teams at two institutions. For both institutions, we will present correlations between individual student characteristics and team outcome. For one institution, we will also present correlation between team-level characteristics and team outcomes.
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This content will become publicly available on June 15, 2026
CliniDial: A Naturally Occurring Multimodal Dialogue Dataset for Team Reflection in Action During Clinical Operation
In clinical operations, teamwork can be the crucial factor that determines the final outcome. Prior studies have shown that sufficient collaboration is the key factor that determines the outcome of an operation. To understand how the team practices teamwork during the operation, we collected CliniDial from simulations of medical operations. CliniDial includes the audio data and its transcriptions, the simulated physiology signals of the patient manikins, and how the team operates from two camera angles. We annotate behavior codes following an existing framework to understand the teamwork process for CliniDial. We pinpoint three main characteristics of our dataset, including its label imbalances, rich and natural interactions, and multiple modalities, and conduct experiments to test existing LLMs’ capabilities on handling data with these characteristics. Experimental results show that CliniDial poses significant challenges to the existing models, inviting future effort on developing methods that can deal with real-world clinical data. We open-source the codebase at https: //github.com/MichiganNLP/CliniDial.†
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- Award ID(s):
- 2202451
- PAR ID:
- 10607996
- Publisher / Repository:
- ACL 2025 Findings
- Date Published:
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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