Project

From Misconception to Mastery

Report by:

Suom Dornuki Nartey

September 13, 2025

How K–12 Learners Understand Interdisciplinary STEAM Concepts: A Systematic Literature Review

Abstract

Background

The growing integration of science, technology, engineering, arts, and mathematics (STEAM) education has prompted calls to examine how K–12 learners conceptualize interdisciplinary content. While research has expanded rapidly, findings remain fragmented regarding how students build understanding, what misconceptions persist, and what contextual factors shape learning outcomes.

Objective

This systematic literature review synthesized empirical studies to answer three research questions: (1) How do K–12 learners understand STEAM concepts? (2) What misconceptions commonly arise? (3) What factors support or hinder conceptual understanding across STEAM domains?

Data Sciences

Peer-reviewed articles published between 2006 and 2025 were identified through Scopus database using combinations of “STEAM,” “conceptual understanding,” “mental models,” and “K–12 learners.” Reference lists of included studies were also screened.

Eligibility Criteria

Studies were included if they (a) focused on K–12 students, (b) examined conceptual understanding or misconceptions in STEAM or integrated STEM/arts contexts, and (c) used empirical methods. The review excluded opinion papers, purely theoretical works, and studies not reporting primary data.

Participants and Study Appraisal

Fourteen studies met inclusion criteria, spanning elementary through high school contexts and employing case studies, quasi-experimental designs, mixed-methods approaches, and qualitative analyses. Quality appraisal considered methodological rigor, clarity of data reporting, and alignment with research questions.

Synthesis of Results

Learners developed deeper conceptual understanding when engaged in hands-on projects, inquiry tasks, coding activities, and visual or arts-based representations that connected abstract ideas to real contexts. Misconceptions such as confusion over particle models in science, linear views of coding, or misapplied proportional reasoning in mathematics were common but functioned as productive “stepping stones” when explicitly addressed through scaffolding, feedback, and peer dialogue. Factors supporting conceptual growth included interdisciplinary project design, teacher expertise, and access to STEAM tools, while barriers included fragmented curricula, rote learning, insufficient teacher training, and resource constraints. Theoretical frameworks such as constructivism, constructionism, and conceptual-change theory explained how students’ mental models evolved toward scientifically accurate conceptions.

Limitations

Most studies were small-scale, cross-sectional, and concentrated in high-income contexts, with limited longitudinal evidence and few contributions from African or other under-researched regions.

Conclusions

Integrated STEAM approaches foster transferable, deep rooted understanding when students actively construct and test knowledge. Addressing misconceptions through guided inquiry and diagnostic assessment enhances learning, while gaps in theory use and geographic representation highlight areas for future research and curriculum development.

References

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Bertrand, M. G., & Namukasa, I. K. (2020). STEAM education: student learning and transferable skills. Journal of Research in Innovative Teaching & Learning, 13(1), 43–56. https://doi.org/10.1108/jrit-01-2020-0003

Brom, C., Yaghobová, A., Drobná, A., Urban, M., Šťastný, D., & diSessa, A. (2025a). Learning about Abstract Systems: Understanding Children’s Journey in Grasping Internet Principles Across Age Groups in a Mixed-Methods Experimental Study. Computers in Human Behavior, 108602–108602. https://doi.org/10.1016/j.chb.2025.108602

Brom, C., Yaghobová, A., Drobná, A., Urban, M., Šťastný, D., & diSessa, A. (2025b). Learning about Abstract Systems: Understanding Children’s Journey in Grasping Internet Principles Across Age Groups in a Mixed-Methods Experimental Study. Computers in Human Behavior, 108602–108602. https://doi.org/10.1016/j.chb.2025.108602

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