Academic Outcomes Among Diverse Community College Students: What Is the Role of Instructor Relationships?

Abstract

Objective: Although much research on community colleges focuses on institutional challenges or student deficits, emerging evidence suggests that student–instructor relationships have the potential to impact student success. The current study examined factors that could influence community college students’ development of relationships with instructors and how these relationships are associated with academic engagement and achievement. Drawing on literature exploring student–instructor relationships at 4-year institutions, we hypothesized that students’ relationships with instructors may partially account for the association between student demographic and relational characteristics and academic outcomes (i.e., cognitive and behavioral engagement, grade point average [GPA]). Method: Survey data were collected from 646 ethnically and racially diverse participants, many of whom were first-, second-, or third-generation immigrants, or first-generation college students. Employing a between-subjects, cross-sectional design, we tested the main study hypotheses of mediation through a series of path analysis models using Mplus. Results: Students with higher support-seeking attitudes and students with a mentor reported closer relationships with instructors, whereas part-time students reported weaker relationships with instructors. In turn, student–instructor relationships were significantly associated with both cognitive and behavioral aspects of academic engagement and GPA. Conclusion: This study provides insight into the various factors that may influence community college students’ development of relationships with instructors and highlights the direct and indirect influence of these relationships on student success. Implications for future practice include finding strategies that can be implemented at community colleges to foster student–instructor relationships. Future research should further explore these associations using longitudinal data to gain a deeper understanding of current findings.