Looking at Our STEM Teacher Workforce: How to Model Self-Efficacy

Brandon Ofem, Samuel J. Polizzi, Gregory T. Rushton, Michael Beeth, Brock Couch, Jessica Doering, Rebecca Konz, Margaret Mohr-Schroeder, Gillian Roehrig, Keith Sheppard

Research output: Contribution to journalArticlepeer-review

Abstract

There is currently a severe shortage of teachers in the U.S. workforce. The problem is especially acute among science, technology, engineering, and mathematics (STEM) teachers and exacerbated by high turnover among new teachers—those with less than 5 years of teaching experience. In this article, the authors investigate one piece of the puzzle. The authors model a social cognitive approach to understanding self-efficacy, a key precursor to job performance and retention. Their interactionist approach accounts for both demographic (i.e., gender and age) and relational variables (i.e., social networks). The authors test their ideas on a sample of 159 STEM teachers across five geographic regions in the United States. Their analysis reveals patterned differences in self-efficacy across gender that are contingent on the communities of practice in which the teachers are embedded. Together, their theory and findings highlight the value of taking a holistic, interactionist view in explaining STEM teacher self-efficacy.

Original languageEnglish (US)
Pages (from-to)40-52
Number of pages13
JournalEconomic Development Quarterly
Volume35
Issue number1
DOIs
StatePublished - Feb 2021

Bibliographical note

Funding Information:
The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported in part by National Science Foundation (NSF) Awards DUE-1035451, DUE-1660665, and DUE-1660736.

Keywords

  • STEM teachers
  • age
  • gender
  • self-efficacy
  • social networks

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