Predicting Team Function Using Bayesian and Cognitive Diagnostic Modeling Approaches
- Conference
- Location
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Baltimore , Maryland
- Publication Date
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June 25, 2023
- Start Date
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June 25, 2023
- End Date
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June 28, 2023
- Conference Session
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First-Year Programs Division (FYP) - Technical Session 6: Mentors & Teams
- Tagged Division
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First-Year Programs Division (FYP)
- Page Count
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16
- DOI
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10.18260/1-2--43937
- Permanent URL
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https://peer.asee.org/43937
- Download Count
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171
Paper Authors
Jeong Hin Chin
University of Michigan
orcid.org/0009-0005-4409-6045
Jeong Hin Chin graduated from the University of Michigan College of Literature, Science, and the Arts with a triple degree in Honors Data Science, Honors Asian Studies, and Statistics. He will be joining the University of Michigan School of Information as a Master's student starting Fall 2023. He is interested in clustering methods, cognitive diagnostic models, educational tools, mHealth, and machine learning.
Jing Ouyang University of Michigan
Jing Ouyang is a Ph.D. Candidate in the Department of Statistics at University of Michigan, supervised by Prof. Gongjun Xu. Before coming to Michigan, I received a BSc. in Mathematics and Economics from the Hong Kong University of Science and Technology in 2019. Her research interests primarily lie in latent variable models, psychometrics, high-dimensional statistical inference and statistical machine learning. Specifically, she is working on developing statistical theory and methodology to analyze high-dimensional and complex data with latent variables for interdisciplinary research.
Robin Fowler
University of Michigan
orcid.org/0000-0001-6161-0986
Robin Fowler is a Technical Communication lecturer and a Engineering Education researcher at the University of Michigan. Her teaching is primarily in team-based engineering courses, and her research focuses on equity in communication and collaboration as well as in group design decision making (judgment) under uncertainty. She is especially interested in how power relationships and rhetorical strategies affect group judgment in engineering design; one goal of this work is to to understand factors that inhibit full participation of students who identify with historically marginalized groups and investigate evidence-based strategies for mitigating these inequities. In addition, she is interested in technology and how specific affordances can change the ways we collaborate, learn, read, and write. Teaching engineering communication allows her to apply this work as she coaches students through collaboration, design thinking, and design communication. She is part of a team of faculty innovators who originated Tandem (tandem.ai.umich.edu), a tool designed to help facilitate equitable and inclusive teamwork environments.
Gongjun Xu University of Michigan
Dr. Gongjun Xu is an Associate Professor in the Department of Statistics with a joint appointment in the Department of Psychology at the University of Michigan.
Rebecca L Matz
University of Michigan
orcid.org/0000-0002-8220-7720
Becky Matz is a Research Scientist on the Research and Analytics team at the Center for Academic Innovation at the University of Michigan. She directs and supports research projects across Academic Innovation’s portfolio of educational technologies and online learning experiences. Prior to joining Academic Innovation, she focused on STEM education assessment and research, connecting faculty with data, and developing interdisciplinary activities for introductory chemistry and biology courses at Michigan State University. Becky earned her Ph.D. in Chemistry and M.S. in Educational Studies from the University of Michigan.
Abstract
Team-based learning is commonly used in engineering introductory courses. As students of a team may be from vastly different backgrounds, academically and non-academically, it is important for faculty members to know what aid or hinder team success. The dataset that is used in this paper includes student personality inputs, self-and-peer-assessments of teamwork, and perceptions of teamwork outcomes. Using this information, we developed several bayesian models that are able to predict if a team is working well. We also constructed and estimated Q-matrices which are crucial in explaining the relationship between latent traits and students’ characteristics in cognitive diagnostic models. The prediction and diagnostic models are able to help faculty members and instructors to gain insights into finding ways to separate students into teams more effectively so that students have a positive team-based learning experience.
Chin, J. H., & Ouyang, J., & Fowler, R., & Xu, G., & Matz, R. L. (2023, June), Predicting Team Function Using Bayesian and Cognitive Diagnostic Modeling Approaches Paper presented at 2023 ASEE Annual Conference & Exposition, Baltimore , Maryland. 10.18260/1-2--43937
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