A 29‐Year (1994–2023) Bibliometrics Analysis of Usage of the Gamification in History Education
Bibliographic record
Abstract
ABSTRACT Over the past two decades, gamification has emerged as a significant pedagogical approach in history education, attracting the attention of historians. This bibliometric study analyses 249 papers from 1994 to 2023, employing scientometric methods to identify trends, key authors, nations, institutions and highly cited documents. Data were processed using Microsoft Excel, Bibliometrix, Publish or Perish (PoP), and VOSviewer. Findings reveal a steady increase in publications, peaking at 26 articles in 2018. VOSviewer identified six research clusters focused on gamification, history education, and history teaching. Elementary school applications emphasise technology, enjoyment, and curiosity, while high school games prioritise analytical skills and critical thinking, utilising online databases, digital archives, and multimedia technologies. The COVID‐19 pandemic further accelerated the growth of the gaming industry, with video games gaining popularity for their roles in social interaction, education, and entertainment. The University of Murcia led in publications, followed by institutions in Greece, Brazil and Taiwan. The United States, Spain and Canada were the most productive countries, with scholars like Seng Yue Wong, Sara de Freitas, Christopher Peters and Panagiotis Petridis emphasising national and academic collaborations over international partnerships. Gamification enhances engagement in history education, but its implementation requires age‐appropriate integration of virtual and augmented reality technologies tailored to learners' developmental characteristics.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.146 | 0.168 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".