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Supporting Multilingual Learners Through Translanguaging Pedagogy in U.S. K–12 STEM Classrooms: A Systematic Meta-Synthesis
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Supporting Multilingual Learners Through Translanguaging Pedagogy in U.S. K–12 STEM Classrooms: A Systematic Meta-Synthesis
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Supporting Multilingual Learners Through Translanguaging Pedagogy in U.S. K–12 STEM Classrooms: A Systematic Meta-Synthesis
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Journal Article
Supporting Multilingual Learners Through Translanguaging Pedagogy in U.S. K–12 STEM Classrooms: A Systematic Meta-Synthesis
2026
Overview
Multilingual learners (MLs) in U.S. schools continue to face systemic inequities shaped by monoglossic instructional ideologies and a deficit orientation towards their linguistic and cultural resources. Translanguaging pedagogy has emerged as a promising response, but it remains underexplored in STEM contexts. This study presents a systematic meta-synthesis of 20 U.S.-based empirical studies examining how translanguaging has been conceptualized and enacted in K–12 STEM classrooms with MLs, using an interpretive approach. The review identified four overarching themes. First, research and practice gaps reveal contextual, conceptual, and disciplinary limitations, particularly a lack of translanguaging work in math, early elementary settings, and English-dominant classrooms. Second, translanguaging was conceptualized as a syncretic and disciplinary practice, challenging rigid boundaries between languages, discourses, and modes while positioning MLs’ full repertoires as generative of disciplinary knowledge. Third, students and teachers were positioned as local agents of knowledge and practice. MLs were framed as designers of disciplinary meaning while teachers acted as collaborators and local policymakers. Fourth, the review identified persisting challenges, including language separation ideologies, narrow interpretations of translanguaging, and policy constraints. This synthesis contributes an interdisciplinary, equity-oriented framework bringing second language acquisition and STEM education, centering MLs as legitimate epistemic participants in STEM.
