Abstract

Background
Teacher communities of practice, identity, and self-efficacy have been proposed to influence positive teacher outcomes in retention, suggesting all three may be related constructs. Qualitative studies of communities of practice can be difficult to empirically link to identity and self-efficacy in larger samples. In this study, we operationalized teacher communities of practice as specific networks related to teaching content and/or pedagogy. This scalable approach allowed us to quantitatively describe communities of practice and explore statistical relationships with other teacher characteristics. We asked whether these community of practice networks were related to identity and self-efficacy, similar to other conceptualizations of communities of practice.

Results
We analyzed survey data from 165 in-service K-12 teachers prepared in science or mathematics at 5 university sites across the USA. Descriptive statistics and exploratory factor analyses indicated that math teachers consistently reported smaller communities of practice and lower identity and self-efficacy scores. Correlations revealed that communities of practice are more strongly and positively related to identity than self-efficacy.

Conclusion
We demonstrate that teacher communities of practice can be described as networks. These community of practice networks are correlated with teacher identity and self-efficacy, similar to published qualitative descriptions of communities of practice. Community of practice networks are therefore a useful research tool for evaluating teacher characteristics such as discipline, identity, self-efficacy, and other possible outcomes (e.g., retention). These findings suggest that teacher educators aiming to foster strong teacher identities could develop pre-service experiences within an explicit, energizing community of practice.

Document Type

Article

Publication Date

4-14-2021

Notes/Citation Information

Published in International Journal of STEM Education, v. 8, article no. 30.

© The Author(s). 2021

This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit https://creativecommons.org/licenses/by/4.0/.

Digital Object Identifier (DOI)

https://doi.org/10.1186/s40594-021-00275-2

Funding Information

This work was supported in part by National Science Foundation (NSF) Awards DUE-1917181, DUE-1660665 and DUE-1660736.

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