Mapping the Global Research Landscape of Misconceptions in Mathematics Learning: A Bibliometric Study
DOI:
https://doi.org/10.51278/aj.v8i4.2817Keywords:
misconceptions, mathematics learning, mathematics education, bibliometric analysis, artificial intelligence, learning analyticsAbstract
This study maps the global research landscape of misconceptions in mathematics learning using bibliometric analysis of Scopus-indexed publications from 2016 to 2026. The study addresses the need to understand how research has evolved from identifying students’ conceptual errors toward assessment, intervention, teacher competence, and technology-supported learning. Data were retrieved from Scopus using the keyword “misconceptions in mathematics learning.” From 596 initially identified records, 151 English-language open-access articles and conference papers met the selection criteria. Bibliometrix/Biblioshiny and VOSviewer were used to analyze publication trends, citation patterns, conceptual structures, thematic evolution, and collaboration networks. The findings show a substantial increase in research output, rising from 3 publications in 2016 to a peak of 31 in 2024, with 1,469 total citations. The intellectual core of the field remains centered on misconceptions, conceptual understanding, and error analysis, while educational computing and computer-aided instruction function as emerging motor themes. Assessment and feedback are gaining prominence, whereas teacher education, teacher knowledge, and lesson study remain weakly integrated. The findings further reveal a persistent gap between diagnosing misconceptions and developing and validating instructional interventions, alongside fragmented author and country collaboration. Based on these patterns, the study proposes an integrated research direction linking diagnosis, diagnostic assessment, conceptual-change-oriented intervention, teacher competence, and pedagogically grounded artificial intelligence. The study provides a global evidence base for strengthening research collaboration and developing more adaptive, evidence-based approaches to reducing mathematical misconceptions.
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