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Service-Learning Community Partner Collaboration Prediction Model

Writer: Jonathan H. Westover, PhDJonathan H. Westover, PhD

Research Advances Section

Submission Date: Jan. 21, 2025; Acceptance Date: Feb 13, 2025; Early Access: March 16, 2025

Title: Service-Learning Community Partner Collaboration Prediction Model

Author: Dr. Jonathan H. Westover, Utah Valley University

​​​​​​​​​​​​​​​​Abstract: Abstract: This study examines the factors influencing community partner collaboration in service-learning projects through a predictive model based on student competencies and project outcomes. Data was collected from 183 community organizations (80.62% response rate) partnering with a large public university in the Intermountain West. The research assessed 13 student professional competencies and 2 project value metrics across 16 service-learning course sections involving 565 students and 12 faculty members from six departments. Using Ordinary Least Squares regression analysis, the study investigated how student professional competencies and project quality influence community partners' likelihood to engage in future collaborations. The findings aim to enhance understanding of successful service-learning partnerships and provide insights for improving program design and implementation. This research contributes to the growing body of knowledge on effective service-learning practices and community engagement in higher education.


Keywords: Service-Learning, Community Partnerships, Student Competencies, Community Engagement, Predictive Modeling, Program Assessment, Experiential Learning, Institutional Collaboration, Gen Z Students, Organizational Partnerships, Project-Based Learning, Community Impact Assessment, Educational Outcomes


Suggested Citation:

Westover, J. H. (2025). Service-learning community partner collaboration prediction model. Transformative Social Impact: A Journal of Community-Based Teaching and Research, 1(1). doi.org/10.70175/socialimpactjournal.2025.1.1.2



Human Capital Leadership Review

eISSN 2693-9452 (online)

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