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Journal : Online Learning in Educational Research

Game-based Learning in Science Education: Bibliometric Analysis Haryandi, Surya; Misbah, Misbah; Arlinda, Rossy; Harto, Muhdi; Muhammad, Nurlaela; Qamariah, Qamariah
Online Learning In Educational Research (OLER) Vol 5, No 1 (2025): Online Learning in Educational Research
Publisher : CV FOUNDAE

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58524/oler.v5i1.626

Abstract

Game-based Learning (GBL) in science education can increase students' interest, motivation, and involvement in the process of learning, so a more in-depth study of GBL through bibliometric analysis is needed. This study aims to analyze research trends on the application GBL in science education through bibliometric analysis from 2021-2024. This study employed bibliometric analysis to examine 349 English-language journal articles published from 2021 to 2024, which were retrieved from the Scopus database. Using VOSviewer, the analysis involved co-occurrence and keyword mapping to identify emerging research themes and publication trends. The results obtained 62 articles for further analysis through bibliometric review and visualization using VOSviewer. The trend of GBL research grows yearly, potentially increasing the number of publications the following year. The majority of GBL-related articles appear in respected international journals. This is also the reason for the article's high citation count. Indonesia ranks second in terms of GBL research. The visualization findings between GBL and science education demonstrate that various keywords under investigation emerged in the previous year. This further indicates a topic worth investigating further. The analysis results are based on the study topic, research location, type of research, and applications utilized in GBL, providing an overview for future researchers to undertake a more in-depth examination of the data. The main findings show that GBL may enhance the learning results of students in the context of science, providing insights into effective learning strategies that educators can adopt. These findings are expected to be used to formulate more innovative and effective educational practices and serve as a basis for further research in this area.
Mapping a Decade of Research on Artificial Intelligence and Augmented Reality in Physics Education: A Bibliometric Analysis (2016–2025) Zainuddin, Zainuddin; Misbah, Misbah; Mastuang, Mastuang; Qamariah, Qamariah; Amiruddin, Mohd Zaidi Bin; Rahman, Nor Farahwahidah Abdul
Online Learning In Educational Research (OLER) Vol. 5 No. 2 (2025): Online Learning in Educational Research
Publisher : CV FOUNDAE

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58524/oler.v5i2.901

Abstract

This study presents a Scopus-based bibliometric mapping of a decade of research on Artificial Intelligence (AI) and Augmented Reality (AR) in physics education, spanning the period from 2016 to 2025. The dataset was retrieved from Scopus on August 2, 2025, and, following PRISMA-style screening and filtering, comprised 1,038 English-language journal articles at the final publication stage. Bibliometric analyses were conducted using Bibliometrix (Biblioshiny), VOSviewer, and Microsoft Excel to examine publication growth, leading sources and authors, geographic and institutional contributions, collaboration patterns, and conceptual structures through keyword co-occurrence, thematic mapping, and thematic evolution. The results indicate accelerated publication growth after 2019 and an interdisciplinary dissemination pattern across education- and technology-facing outlets. Conceptual mapping suggests that AI-related themes (e.g., adaptive and data-informed learning support) and AR-related themes (e.g., interactive visualization and representational learning) constitute the dominant pillars of the field, while physics-education-specific learning mechanisms (e.g., conceptual change, multi-representational reasoning, and inquiry/laboratory enactment) are unevenly foregrounded across clusters. Because this is a bibliometric study, the findings provide a structured overview of research patterns and thematic orientations rather than causal evidence of learning effectiveness, thereby informing future empirical and design-based studies that connect AI/AR developments to physics-education-specific learning mechanisms and implementation contexts.