Mapping the social side of pre-service teachers: connecting closeness, trust, and efficacy with performance

Abstract

This exploratory study foregrounds the important, but often understudied social side of pre-service teacher development and its relation to teaching performance in one university-based teacher preparation program in the US. We examine the extent to which pre-service elementary teachers’ social relationships and perceptions of peer trust and efficacy are associated with performance on a high stakes mathematics teaching assessment. Findings suggest that social and emotional support through close social ties, peer trust, and self-efficacy are significantly and positively associated with pre-service teachers’ teaching performance. Our work further contributes to the development and discourse about teacher education in university-based teacher preparation programs.

Keywords:

• Pre-service teacher education
• social capital
• social networks
• peer trust
• efficacy and performance

Disclosure statement

No potential conflict of interest was reported by the authors.

Notes

1. For detailed information regarding the assessment items, please refer to http://www.pacttpa.org/.

2. It is noteworthy that we also investigated teaching performance in science, language arts, and history and social science, and our models indicated a consistent pattern of predictors in explaining per-service teachers’ outcome performance. For instance, we found the pre-service teachers’ science teaching performance is significantly associated with peer trust, self-efficacy, and incloseness and indegree with adjusted R² of .35 (F = 5.24, p < .01). As for teaching performance in history and social science, the model indicates a series of significant relationships between the outcome variable and peer trust, self-efficacy, and degree centrality with adjusted R² of .23 (F = 3.38, p < .05). In the current study, to provide more focused study results, we reported findings from mathematics teaching performance.

3. Generally, the VIF of 10 is has been proposed as a cut-off value. If the value of VIF is greater than 10, it is likely that the multicollinearity is influencing the least squares point estimates.