BIBLIOMETRIC ANALYSIS OF DIGITAL TOOLS IN MATHEMATICS EDUCATION: TRENDS, COUNTRIES, AND EMERGING KEYWORDS
Bibliographic record
Abstract
This work undertakes a bibliometric study and analysis of the scientific literature dealing with digital tools in teaching mathematics from 2000 to 2025, sourced from IEEE Xplore. Its goal is to shed light on thematic trends, geographical research impact, and common keywords, titles, and co-authors in this very rapidly evolving world. The data set contains 1,152 scientific contributions, mostly conference papers (80.38%), reflecting the dynamic and ever-changing nature of the field. Keyword analysis unveiled five thematic clusters, indicating the integration of STEM, AI, and cybersecurity in education methodologies. The United States remains at the top in terms of citation and h-index, followed by China, Israel, India, and Canada. Within the Balkans, Turkey enjoys scientific supremacy, while North Macedonia seems starved of recognition, with a single publication to its account, and none cited. According to the results, the research interest is shifting away from work on traditional educational constructs to those on artificial intelligence in education and science.This work undertakes a bibliometric study and analysis of the scientific literature dealing with digital tools in teaching mathematics from 2000 to 2025, sourced from IEEE Xplore. Its goal is to shed light on thematic trends, geographical research impact, and common keywords, titles, and co-authors in this very rapidly evolving world. The data set contains 1,152 scientific contributions, mostly conference papers (80.38%), reflecting the dynamic and ever-changing nature of the field. Keyword analysis unveiled five thematic clusters, indicating the integration of STEM, AI, and cybersecurity in education methodologies. The United States remains at the top in terms of citation and h-index, followed by China, Israel, India, and Canada. Within the Balkans, Turkey enjoys scientific supremacy, while North Macedonia seems starved of recognition, with a single publication to its account, and none cited. According to the results, the research interest is shifting away from work on traditional educational constructs to those on artificial intelligence in education and science.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.052 | 0.131 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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; both teacher heads agree on what is shown here.
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".