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Introducing junto: a Web Tool to Build Project Teams based on a Bidding Strategy

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Conference

2020 ASEE Virtual Annual Conference Content Access

Location

Virtual On line

Publication Date

June 22, 2020

Start Date

June 22, 2020

End Date

June 26, 2021

Conference Session

Design Teams 1

Tagged Division

Design in Engineering Education

Tagged Topic

Diversity

Page Count

12

DOI

10.18260/1-2--34876

Permanent URL

https://peer.asee.org/34876

Download Count

144

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Paper Authors

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Akhil Krishna Mohan University of Illinois at Urbana-Champaign

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Mathematics and Computer Science major, Class of 2021.

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Priyanka Dey University of Illinois at Urbana-Champaign

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Priyanka Dey is currently pursuing a B.S. degree in Computer Science Statistics. Her research interests include natural language processing, data mining, and optimization algorithms.

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Sizhi Tan University of Illinois at Urbana-Champaign

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Sizhi Tan is currently pursuing a Bachelor's Degree in Computer Science at the University of Illinois at Urbana Champaign.

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Blake Everett Johnson University of Illinois at Urbana-Champaign

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Dr. Blake Everett Johnson is a lecturer and instructional laboratory manager in the Department of Mechanical Science and Engineering at the University of Illinois at Urbana-Champaign. His research interests include experimental fluid mechanics, measurement science, and engineering education. He oversees undergraduate laboratories in fluid mechanics, fluid dynamics, and heat transfer. Pedagogically, Dr. Johnson employs active learning, inquiry-based laboratory instruction, and any initiative that empowers students to do hands-on learning. Additional service interests include teaching and leadership training for graduate students, enhancing communication education for undergraduate engineering students, developing evidence-based design project team formation strategies, and improving engineering design curricula.

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Wade Fagen-Ulmschneider University of Illinois at Urbana-Champaign Orcid 16x16 orcid.org/0000-0002-7313-7708

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Wade Fagen-Ulmschneider is a Teaching Associate Professor of Computer Science at the University of Illinois at Urbana-Champaign (UIUC). With a passion for data, he teaches thousands of students each year in his courses on Data Structures, Data Visualization, and Data Science. He was selected as one of the National Academy of Engineering’s Frontiers of Engineering Education scholars, awarded the Collins Award for Innovation Teaching, and has been consistently ranked as an excellent instructor by his students for the past ten years. His work on data visualizations has been used by governors of multiple states, featured by websites including Popular Mechanics and The Verge, and has been viewed by millions of readers.

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Mariana Silva University of Illinois at Urbana-Champaign

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Mariana Silva is a Teaching Assistant Professor in Computer Science at the University of Illinois at Urbana-Champaign. She has been involved in large-scale teaching innovation activities, such as the development of online course content and assessments for the mechanics course sequence in the Mechanical Science and Engineering Department and the numerical methods class in Computer Science. Silva is currently involved in two educational projects involving the development of online assessments for computer-based testing and creation of collaborative programming activities for computer science classes. She is also involved in a project that aims to create a software that facilitates collaborative problem-solving activities in classrooms, through which both the instructors and students learn more about collaboration skills. Silva is very passionate about teaching and improving the classroom experience for both students and instructors. She has been included in the List of Teachers Ranked as Excellent five times and has received the Engineering Council Outstanding Advisor Award every year since 2014.

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Abstract

This work presents a web application created to help instructors assign students to group projects, with an approach that optimizes student satisfaction, gives students the opportunity to select a team member, and reduces the instructor time creating the teams. Previous work that analyzed data from senior capstone teams indicate that team composition is one of the most important factors that contribute to student satisfaction [1], and for that reason many authors have proposed methods to better distribute students in teams corresponding to predetermined projects [2–4]. Our approach focuses on three main aspects: (a) it gives the student the ability to apply weights to their project choices (instead of just ranking them), (b) it provides students with the opportunity to select a classmate to be partnered with, and (c) it creates an open-source web-based tool that can be used by others.

Our tool consists of an implementation of a genetic algorithm (GA) that assigns students to projects in order to maximize the fitness function, defined as a multi-objective function to increase student satisfaction, decrease the variance of team sizes, and optionally decrease the GPA variance among team members. To achieve this, students view a list of available projects and must allocate “points” across a minimum number of projects based on their personal interest in the project. For example, in one course students assigned 15 points across a minimum of 6 projects. In addition to the individual points, instructors can require certain student-project assignments and instructors can allow students to allocate their points as a team to ensure they are on the same project.

This GA tool has been used by instructors of the capstone design course in a mechanical engineering program for over 5 years, impacting more than 1100 students. Instructors reported that the previous process of manually creating the teams involved a total of 1–2 days of work, and that they are now able to assign all of the students to project teams in less than two hours. Students are no longer assigned to projects that they did not select, and about 80% of the students are assigned to projects they had given a high weight. In surveys, students report they are highly satisfied with their team and project assignments at both the beginning of the semester and after the semester. Indeed, the distribution of student satisfaction with their team assignment becomes even more positive upon completion of the course. Students also indicate informally that they appreciate being able to select one classmate to be part of their team.

[1] Bosco et al (2009): “The effect of team selection method on the occurrence and nature of conflict”, Journal of Applied Research for Business Instructions, vol 7(1), 2009. [2] Michaelis, B. and Bae, H. (2019): “Optimizing Capstone Team Selection”, ASEE 2019. [3] Freiheit, T. and Wood J. (2007): “An algorithm for project assignment in capstone design”, ASEE 2007 [4] Schmidt et al. (2011): “An optimized routine for assigning students to capstone project groups”, ASEE 2011.

Mohan, A. K., & Dey, P., & Tan, S., & Johnson, B. E., & Fagen-Ulmschneider, W., & Silva, M. (2020, June), Introducing junto: a Web Tool to Build Project Teams based on a Bidding Strategy Paper presented at 2020 ASEE Virtual Annual Conference Content Access, Virtual On line . 10.18260/1-2--34876

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