Strategies for Efficacy and Belongingness towards Foundational STEM Skills for At-Promise Graduate Students: The QMER Learning Community

Increasing representation of first-generation, low-income, and students of color has been considered critical in developing an inclusive and competitive future workforce. However, research has noted significant difficulties with continued engagement and persistence for those engaged in STEM-based activities, with literature showing at-promise students face difficulties in two areas: (1) lack of basic exposure to processes and norms undergirding advanced material, and (2) low self-conception surrounding issues of efficacy and belongingness. This paper reports on the findings of implementing a STEM-focused learning community in a flagship Southeastern university to improve graduate student research efficacy and social belongingness. The learning community program was aimed at (1) broadly supporting foundational STEM skills through supplementary and specialized curriculum; and (2) providing social and professional supports geared towards at-promise students. Survey and interview results demonstrate that graduate students participating in the learning community reported high senses of self-efficacy and belongingness. In addition, low-income, first-generation, and Black graduate students reported higher levels of self-efficacy and belongingness on average. This paper concludes that the learning community approach can be highly beneficial in developing research efficacy across the board, but at-promise students specifically benefit from professional skills development to undergird and contextualize their research confidence.

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Publication Details

Journal
Current Issues in Education
Published
2025-05-02
DOI
https://doi.org/10.14507/cie.vol26iss1.2260
Primary Topic
Teaching and Learning Programming
Type
article
Field-Weighted Citation Impact
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article

Strategies for Efficacy and Belongingness towards Foundational STEM Skills for At-Promise Graduate Students: The QMER Learning Community

William M. Murrah, Andrew Pendola, John Appiah, Clarissa Beavers
Current Issues in Education
Teaching and Learning Programming
article

Strategies for Efficacy and Belongingness towards Foundational STEM Skills for At-Promise Graduate Students: The QMER Learning Community

William M. Murrah, Andrew Pendola, John Appiah, Clarissa Beavers
article en

Abstract

Increasing representation of first-generation, low-income, and students of color has been considered critical in developing an inclusive and competitive future workforce. However, research has noted significant difficulties with continued engagement and persistence for those engaged in STEM-based activities, with literature showing at-promise students face difficulties in two areas: (1) lack of basic exposure to processes and norms undergirding advanced material, and (2) low self-conception surrounding issues of efficacy and belongingness. This paper reports on the findings of implementing a STEM-focused learning community in a flagship Southeastern university to improve graduate student research efficacy and social belongingness. The learning community program was aimed at (1) broadly supporting foundational STEM skills through supplementary and specialized curriculum; and (2) providing social and professional supports geared towards at-promise students. Survey and interview results demonstrate that graduate students participating in the learning community reported high senses of self-efficacy and belongingness. In addition, low-income, first-generation, and Black graduate students reported higher levels of self-efficacy and belongingness on average. This paper concludes that the learning community approach can be highly beneficial in developing research efficacy across the board, but at-promise students specifically benefit from professional skills development to undergird and contextualize their research confidence.

Current Issues in EducationVol. 26(1)
University of Cape Coast (GH), Auburn University (US)
Openalex Percentile: Top 91%
Teaching and Learning Programming
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