Micro-credentials in higher education: a review and bibliometric
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.014 | 0.011 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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 it