Scientometric Analysis of Technology-Enhanced Learning Research
Bibliographic record
Abstract
This study presents a comprehensive scientometric analysis of Technology-Enhanced Learning (TEL) research from 2020 to 2024, examining publication trends, influential institutions and researchers, research themes, citation patterns, and geographical distribution. The analysis of 3,276 articles from the Lens database reveals a consistent growth in TEL publications, with journal articles dominating the landscape. The University of Melbourne, Auckland University of Technology, and the University of Hong Kong have emerged as leading institutions in TEL research. Notable researchers such as Hiroaki Ogata and Brett Bligh have made significant contributions to the field. The International Journal of Technology is a primary dissemination platform for TEL research. The thematic analysis identifies simulation, virtual reality, and e-learning as central topics, with an emerging interest in gamification and educational robotics. The study highlights the interdisciplinary nature of TEL, spanning computer science, psychology, and mathematics education. Citation network analysis reveals influential works, particularly Chrysi Rapanta’s 2020 study, which has shaped subsequent research trajectories. Geographically, research activity is concentrated in North America, Europe, and Asia, with potential for growth in Africa and South America. A positive trend towards open-access publications suggests increasing accessibility of TEL research. This scientometric analysis provides valuable insights into the current state and future directions of TEL research, emphasizing the need for cross-institutional collaboration and addressing global educational challenges through technological innovation balanced with sound pedagogical principles.
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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 | 0.022 | 0.115 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.171 | 0.224 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".