Student Scholar Symposium Abstracts and Posters
Document Type
Poster
Publication Date
Spring 5-6-2026
Faculty Advisor(s)
Dr. LouAnne Boyd
Abstract
Effective teamwork is essential for collaborative learning, yet forming compatible student groups remains a persistent challenge. This study explores how algorithmic team-building methods influence students’ social and academic compatibility overtime. To support this investigation, we designed a visual survey system called “The Disco Ball." Evaluating work ethics, lifestyles, behaviors, and communication styles, students were initially assigned to project teams using one of three conditions: (1) random assignment (control), (2) a “most matches” condition maximizing similarity between students, and (3) a “diverse communication styles” condition designed to balance differing interaction preferences. After an initial collaboration period, students were given the opportunity to reorganize into self-selected groups. Using exploratory visuals, statistical tests, and MR-QAP modeling, we examine how initial algorithmic assignments relate to long-term group persistence, reorganization, and collaboration patterns. We hypothesize that different grouping strategies will produce distinct outcomes in team stability and productivity, with communication-style diversity shaping how groups evolve over time. This work contributes to research on human-centered team formation and offers insights into designing more effective and inclusive collaborative environments.
Recommended Citation
Lopez, Ethan E. and Vo, Andy, "Evaluating Team Formation Strategies With Algorithms" (2026). Student Scholar Symposium Abstracts and Posters. 821.
https://digitalcommons.chapman.edu/cusrd_abstracts/821
Included in
Applied Behavior Analysis Commons, Community-Based Research Commons, Interpersonal and Small Group Communication Commons, Organizational Communication Commons, Organization Development Commons, Other Computer Engineering Commons, Other Social and Behavioral Sciences Commons, Theory, Knowledge and Science Commons
Comments
Presented at the Spring 2026 Student Scholar Symposium at Chapman University.
This project is currently undergoing continued validation and refinement through additional testing, expanded datasets, and further analysis of long-term collaboration outcomes. Future work will focus on improving the grouping algorithms, evaluating broader student populations, refining app interfaces, and strengthening the reliability of findings related to team stability, communication dynamics, and productivity.