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Title: A Learner-Centered Technique for Collectively Configuring Inputs for an Algorithmic Team Formation Tool
The configuration that an instructor enters into an algorithmic team formation tool determines how students are grouped into teams, impacting their learning experiences. One way to decide the configuration is to solicit input from the students. Prior work has investigated the criteria students prefer for team formation, but has not studied how students prioritize the criteria or to what degree students agree with each other. This paper describes a workflow for gathering student preferences for how to weight the criteria entered into a team formation tool, and presents the results of a study in which the workflow was implemented in four semesters of the same project-based design course. In the most recent semester, the workflow was supplemented with an online peer discussion to learn about students' rationale for their selections. Our results show that students want to be grouped with other students who share the same course commitment and compatible schedules the most. Students prioritize demographic attributes next, and then task skills such as programming needed for the project work. We found these outcomes to be consistent in each instance of the course. Instructors can use our results to guide team formation in their own project-based design courses and replicate our workflow to gather student preferences for team formation in any course.  more » « less
Award ID(s):
2016908
NSF-PAR ID:
10350670
Author(s) / Creator(s):
; ; ;
Date Published:
Journal Name:
Proceedings of the 53rd ACM Technical Symposium on Computer Science Education V. 1 (SIGCSE 2022)
Volume:
1
Page Range / eLocation ID:
969 to 975
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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