Micro-credentials in higher education: a review and bibliometric
Bibliographic record
Abstract
The objective of this study is to conduct a comprehensive review of research on “Micro-credentials in higher education” by doing a bibliometric analysis of 85 journal articles published between 2015 and 2023, obtained from the Scopus database. This study focuses on quantifying the number of publications and citations, as well as examining subject areas, connections, universities, countries, and identifying the most productive and prominent researchers. Apart from that, this research also identifies research topics that researchers have been working on in recent years. The findings show that publications and citations have increased in the last three years. The United States, Australia, and Canada are the most productive countries on this topic. T. J. Newby is the most productive researcher, while the most influential writer is D. -K. Mah. TechTrends and The International Journal of Information and Learning Technology are the journals that publish the most research. The university that made the top contribution was Purdue University (United States). The results of data analysis show that collaboration between authors researching “Micro-credentials in higher education” still needs improvement. This research contributes as a basis for further research in enriching and developing knowledge about micro-credentials, especially in higher education.
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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 | 0.007 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.033 | 0.048 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".