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Design and Validation of a System to Assign Students to Projects Based on Student Preferences

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Conference

2021 ASEE Virtual Annual Conference Content Access

Location

Virtual Conference

Publication Date

July 26, 2021

Start Date

July 26, 2021

End Date

July 19, 2022

Conference Session

Design Teams 1

Tagged Division

Design in Engineering Education

Page Count

17

DOI

10.18260/1-2--36914

Permanent URL

https://peer.asee.org/36914

Download Count

32

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Paper Authors

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Siqing Wei Purdue University, West Lafayette Orcid 16x16 orcid.org/0000-0002-7086-5953

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Siqing Wei received BSEE and MSEE from Purdue University. He is currently pursuing a Ph.D. degree in Engineering Education program at Purdue University. After years of experience of serving the peer teacher and a graduate teaching assistant in first-year-engineering courses, he is now a research assistant at CATME research group studying how cultural diversity impacts teamwork and how to help students improve intercultural competency and teamwork competency by interventions, counseling, pedagogy, and tool selection (such as how to use CATME Team-Maker to form inclusive and diversified teams). In addition, he also works on many research-to-practice projects to enhance educational technology usage in engineering classrooms and educational research. One feature ongoing project utilizes natural language processing technique to map students' written peer-to-peer comments with their perceived numerical ratings. Siqing also works as the technical development and support manager at CATME research group.

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Amarto Pramanik Purdue University, West Lafayette

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Matthew W. Ohland Purdue University, West Lafayette Orcid 16x16 orcid.org/0000-0003-4052-1452

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Matthew W. Ohland is Associate Head and the Dale and Suzi Gallagher of Professor of Engineering Education at Purdue University. He has degrees from Swarthmore College, Rensselaer Polytechnic Institute, and the University of Florida. His research on the longitudinal study of engineering students, team assignment, peer evaluation, and active and collaborative teaching methods has been supported by the National Science Foundation and the Sloan Foundation and his team received for the best paper published in the Journal of Engineering Education in 2008, 2011, and 2019 and from the IEEE Transactions on Education in 2011 and 2015. Dr. Ohland is an ABET Program Evaluator for ASEE. He was the 2002–2006 President of Tau Beta Pi and is a Fellow of the ASEE, IEEE, and AAAS.

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Daniel M. Ferguson Purdue University, West Lafayette

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Daniel M. Ferguson is CATME Managing Director in the school of engineering education, college of engineering and the recipient of several NSF awards for research in engineering education including his own research in engineering innovativeness and a research associate at Purdue University. Prior to coming to Purdue he was Assistant Professor of Entrepreneurship at Ohio Northern University. Before assuming that position he was Associate Director of the Inter-Professional capstone Studies Program [IPRO] and Senior Lecturer at Illinois Institute of Technology and involved in research in service learning, assessment processes and interventions aimed at improving learning objective attainment. Prior to his University assignments he was the Founder and CEO of The EDI Group, Ltd. and The EDI Group Canada, Ltd, independent professional services companies specializing in B2B electronic commerce and electronic data interchange. The EDI Group companies conducted syndicated market research, offered educational seminars and conferences and published The Journal of Electronic Commerce. He was also a Vice President at the First National Bank of Chicago [now part of J.P. Morgan Chase], where he founded and managed the bank’s market leading professional Cash Management Consulting Group, initiated the bank’s non-credit service product management organization and profit center profitability programs and was instrumental in the breakthrough EDI/EFT payment system implemented by General Motors. Dr. Ferguson is a graduate of Notre Dame, Stanford and Purdue Universities, a special edition editor of the Journal of Engineering Entrepreneurship and a member of Tau Beta Pi.

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Abstract

Automating team assignments for project-based courses has the potential to significantly improve efficiency and effectiveness of the process by freeing up instructor time, improving the average match preference, and reducing the variability of preference matching. This paper discusses the design and testing for a new project preference matching algorithm as part of Team-Maker system of CATME Team Tools. A new ranking question type in the Team-Maker system allows students to rank a list of projects from a scale of most desired to least desired. The ranking algorithm attempts to match students with the project of the highest possible rank. Initial testing finds that the algorithm places more than 95% of students into one of their top 3 projects under ideal conditions—when there is an equal number of teams and projects when all projects are considered equally desirable, and when project preference is the only team formation criterion. When used in combination with the other team formation criteria in the Team-Maker system, the ranking algorithm performs less effectively. This is especially true when those other criteria have similar weights to that of the ranking question. Nevertheless, when other criteria had a low weight relative to project preference, the algorithm still placed more than 80% of students to their top 3 projects. Algorithm testing so far has relied on randomly generated student preferences, however. Empirical research is still needed to validate the effectiveness of this new feature with real students and project choices. It would also be imperative to investigate the effects of different combinations of criteria and associated weights to propose the optimal team-making strategy for instructors.

Wei, S., & Pramanik, A., & Ohland, M. W., & Ferguson, D. M. (2021, July), Design and Validation of a System to Assign Students to Projects Based on Student Preferences Paper presented at 2021 ASEE Virtual Annual Conference Content Access, Virtual Conference. 10.18260/1-2--36914

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