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Addressing the educational challenges of urban poverty: a case for solution-based research

INTRODUCTION: Math achievement for economically disadvantaged students remains low, despite positive developments in research, pedagogy, and funding. In the current paper, we focused on the research-to-practice divide as possible culprit. Our argument is that urban-poverty schools lack the stability...

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Detalles Bibliográficos
Autores principales: Cartwright, Macey, O'Callaghan, Erin, Stacy, Sara, Hord, Casey, Kloos, Heidi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10213927/
https://www.ncbi.nlm.nih.gov/pubmed/37252445
http://dx.doi.org/10.3389/frma.2023.981837
Descripción
Sumario:INTRODUCTION: Math achievement for economically disadvantaged students remains low, despite positive developments in research, pedagogy, and funding. In the current paper, we focused on the research-to-practice divide as possible culprit. Our argument is that urban-poverty schools lack the stability that is necessary to deploy the trusted methodology of hypothesis-testing. Thus, a type of efficacy methodology is needed that could accommodate instability. METHOD: We explore the details of such a methodology, building on already existing emancipatory methodologies. Central to the proposed solution-based research (SBR) is a commitment to the learning of participating students. This commitment is supplemented with a strength-and-weaknesses analysis to curtail researcher bias. And it is supplemented with an analysis of idiosyncratic factors to determine generalizability. As proof of concept, we tried out SBR to test the efficacy of an afterschool math program. RESULTS: We found the SBR produced insights about learning opportunities and barrier that would not be known otherwise. At the same time, we found that hypothesis-testing remains superior in establishing generalizability. DISCUSSION: Our findings call for further work on how to establish generalizability in inherently unstable settings.