Jul 2026· International Electronic Journal of Mathematics Education· 0 citations· 173 references
Abstract
Given the diversity of theoretical approaches to mathematics learning, this study aims to characterize the predominant theoretical perspectives on the understanding of mathematical concepts within the scientific literature, as well as to identify the collaboration networks and intellectual structure of research in this field. A bibliometric and social network analysis was conducted on 4,266 records from the Scopus database (1980–2024). The findings reveal a dual structure: while the discipline’s intellectual base remains anchored in educational psychology theories (Pekrun, Eccles), the current research front has evolved toward specialized didactic frameworks such as the Onto-Semiotic Approach and Embodied Cognition. The results indicate that the discipline is undergoing a phase of professionalization and theoretical specialization, although high fragmentation and a critical geographic gap persist, excluding emerging regions like Latin America from central collaboration nodes. It is concluded that an epistemological transition is occurring, moving from classical socio-constructivism toward neuro-cognitive and multimodal approaches. These findings underscore the need to integrate fragmented theoretical frameworks and promote transregional collaboration policies to diversify global theoretical production.
Self-efficacy has been one of the most widely used psychological constructs in mathematics education research for over three decades. However, the development of research, the intellectual structure of the field, and its relationship to learning approaches and mathematical competence have not been comprehensively developed. This study aims to analyze publication trends, thematic structures, and identify research gaps regarding self-efficacy in mathematics education. The study uses a bibliometric approach to 3,096 Scopus Open Access documents from 2013–2025 that have been screened to ensure their relevance to the field of education. The analysis was conducted using Bibliometrix (R) to broadcast publication performance and VOSviewer to map the co-word network. The results show sharp and consistent growth (Annual Growth Rate 20.47%), with Frontiers in Psychology as the most published platform (197 documents) while EURASIA Journal of Mathematics, Science and Technology Education remains the most relevant core journal for the mathematics education audience specifically. The co-word mapping identified four thematic clusters—self-efficacy, mathematics teaching and achievement; health and behavior; clinical education and medical competence; as well as procedures and pilot studies. However, the most pedagogically significant finding is the lack of explicit integration between self-efficacy, team-based learning, and mathematical communication in the context of statistics education: combined phrases such as ‘collaborative learning in statistics’ and ‘communication skills in mathematics’ were completely absent (n = 0) among all the documents analyzed, while the individual terms rarely appeared alongside self-efficacy. This finding confirms that research interventions that integrate collaborative learning with self-efficacy strengthening in statistics courses—particularly in higher education contexts that are still underrepresented in the global literature such as Southeast Asia—represent a pressing research agenda and have the potential to make significant pedagogical contributions to the teaching of mathematics and statistics in higher education.
Nike Astiswijaya, T. Turmudi, Kusnandi Kusnandi et al.· ISEJ : Indonesian Science Ed...· 0 citations
Research on Contextual Teaching and Learning (CTL) in elementary mathematics education has expanded substantially over the past decade, yet no bibliometric study has systematically mapped its intellectual structure, growth pattern, or geographic distribution. This study addresses that gap through a bibliometric performance analysis of 55 Scopus-indexed documents published between 2009 and 2026, guided by three research questions on publication trends, geographic contributions, and source characteristics. Data were retrieved from the Scopus database on May 2, 2026, and analyzed using the Scopus Analyze Search Results feature and Microsoft Excel. Results reveal that annual output grew from one document in 2009 to a peak of 11 in 2020 (20.0% of total), declined to two in 2022 amid COVID-19 disruptions, and resurged to 10 in 2025, the second highest on record. Geographically, Indonesia accounts for 85.5% of publications (n = 47), followed by Malaysia (5.5%; n = 3), while contributions from Western countries and other regions remain negligible. Publication sources are dominated by conference proceedings: the Journal of Physics: Conference Series (36.4%) and AIP Conference Proceedings (20.0%) together account for 56.4% of all documents. These findings highlight the need for broader international collaboration, stronger journal-based dissemination, and expanded use of advanced bibliometric tools.
Research on mathematics learning interest in primary education has grown rapidly over the past two decades, yet no bibliometric study has systematically mapped its intellectual structure and thematic evolution. This study aimed to map the research landscape of mathematics learning interest in primary education by identifying publication trends, research productivity, and dominant research themes. A bibliometric analysis was conducted on 92 articles indexed in Scopus from 1982 to 2025 using Bibliometrix and VOSviewer. Findings reveal an annual growth rate of 6.66% with a significant surge after 2018, with China, the United States, and Turkey dominating research output. Nine thematic clusters were identified, with dominant themes including mathematics achievement and anxiety, motivation and learning interest, and self-concept and self-efficacy. The fragmented co-authorship network indicates limited international collaboration in this field. These findings provide a comprehensive intellectual map for researchers and education practitioners to identify research gaps and build a more structured collaborative agenda for future inquiry.
Research on mathematics learning interest in primary education has grown rapidly over the past two decades, yet no bibliometric study has systematically mapped its intellectual structure and thematic evolution. This study aimed to map the research landscape of mathematics learning interest in primary education by identifying publication trends, research productivity, and dominant research themes. A bibliometric analysis was conducted on 92 articles indexed in Scopus from 1982 to 2025 using Bibliometrix and VOSviewer. Findings reveal an annual growth rate of 6.66% with a significant surge after 2018, with China, the United States, and Turkey dominating research output. Nine thematic clusters were identified, with dominant themes including mathematics achievement and anxiety, motivation and learning interest, and self-concept and self-efficacy. The fragmented co-authorship network indicates limited international collaboration in this field. These findings provide a comprehensive intellectual map for researchers and education practitioners to identify research gaps and build a more structured collaborative agenda for future inquiry.
Aprina S. P. Hutagalung, Lutfi Nur, G. Hamdu· Jurnal Pembelajaran Bimbinga...· 0 citations
Personality is a fundamental factor in shaping classroom dynamics, student engagement, and instructional paradigms, yet introversion remains a highly fragmented and peripheral topic within educational scholarship. This study systematically applies a macro-level bibliometric approach to map the latent environment of introversion in the context of the more general literature of teaching and learning. We performed a series of Boolean searches in the Scopus database to reach a final dataset of 753 peer-reviewed publications published from 2000 to 2025. We employed the VOS viewer tool for network visualization and algorithmic clustering to analyze publication trajectories, citation networks, and thematic intersections. Bibliographic coupling revealed five distinct clusters mapping the structural anchors of the field, dominated by research streams in psychological well-being, adaptive learning systems, and academic motivation. Concurrently, co-word analysis yielded four primary thematic networks. Keyword co-occurrence mapping demonstrates that while the literature is overwhelmingly structured around broad macro-level frameworks (n=353), introversion remains situated on the structural periphery (n=21). While this quantitative difference reflects the deliberate scope of our broader search strategy, it visually maps the degree to which introversion remains subsumed under generalized personality constructs in educational scholarship. This empirical difference suggests that educational research has tended to examine personality through broader constructs, with introversion receiving comparatively limited explicit attention. Obscuring the individualized teaching realities of introverted educators. In this paper, we bring these networks together through an integrative matrix to map the implicit embedding of introverted teaching qualities, including deliberate reflection, deep listening, and asynchronous digital mediation, within mainstream pedagogies. Ultimately, this research provides a structural framework for the conversion of introversion from a stigmatized variable to an explicitly acknowledged strength in the design of contemporary education.
Marylyn Inocencio· The International Review of...· 0 citations
This study presents a multidimensional bibliometric and thematic analysis of L2 vocabulary learning research in higher education from 1992 to 2025. Leveraging a two-database framework that integrates data from Scopus and the Web of Science Core Collection, the paper applies Hallinger and Kovačević’s Four-Dimensional Bibliometric Framework to analyze the evolution, intellectual landscape, and methodological trends of the field. 557 peer-reviewed journal articles were quantitatively mapped, and thematic clusters were identified using Bibliometrix (R) and VOSviewer. The findings reveal three stages of scholarly development, diversification of institutions and regions involved, and varying methodological orientations between databases. Seven thematic clusters emerged, including technology-enhanced vocabulary instruction, modeling vocabulary knowledge, and incidental acquisition processes. In addition to validating dominant research paradigms, the paper places special focus on emerging and yet-to-be-explored domains such as informal digital learning, psychological network analysis, and vocabulary pedagogy for equity. A comparison of Scopus and WoS suggests a need for dual-source indexing to justify large-scale bibliometric synthesis. The study concludes by proposing an agenda for future research that is inclusive in methodology, theory-driven, and context-sensitive in its responsiveness to global language learning dynamics.
This study investigates the evolution and conceptual framework of research connecting artificial intelligence (AI) with scientific thinking in science education. Utilizing a dataset of 21,874 publications retrieved from the Web of Science, a bibliometric analysis was conducted to examine publication trends, disciplinary distributions, international collaborations, and their alignment with the United Nations (UN) Sustainable Development Goals (SDGs). The results indicate a rapid acceleration of research output post-2019, marking the consolidation of AI-related inquiry within educational and cognitive domains. Although the field remains anchored in computer science and engineering, there is a notable rise in interdisciplinarity through contributions from education, psychology, and philosophy. Global participation is concentrated in technologically advanced regions, while institutional patterns highlight collaborative networks among leading universities and research centers. SDG classifications reveal connections with health, education, innovation, and environmental sustainability. Overall, the study depicts a dynamically expanding domain where AI functions not only as a technological innovation but also as an epistemic framework that reshapes reasoning, inquiry, and reflective judgment in science education. The findings offer both a conceptual and empirical basis for linking technological capabilities with the epistemic and ethical dimensions of learning in an AI-driven world.
Konstantinos Karampelas· International Journal of Edu...· 0 citations