Emerging Trends of Self-regulated Learning: A Comprehensive Bibliometric Analysis
Bibliographic record
Abstract
Barry J Zimmerman considered that self-regulated learning is an active learning process including strategy use, metacognition, and motivation. Self-regulation failure is the core problem of academic procrastination, which seriously threatens academic success. The present research aims to provide a complete outline of SRL and catch the trends and current hotspots. VOSviewer and Bibliometric software was used to analyze the data from the Web of Science core collection database. The results showed that there are a considerable number of publications of articles on self-regulated learning every year; the USA is the most influential country, and Maastricht University is the most productive institution; it is clear that most authors do not like to cooperate with others, which leads to major groups of writers like Azevedo Roger and Gasevic Dragan. The Frontiers in Psychology is an influential journal that received much more articles and citations. The main hotspots of self-regulated learning were: (a) self-regulated learning; (b) self-regulation; (c) metacognition; (d) motivation; (e) learning analytics. In a word, this study is useful for practitioners and scholars to comprehensively understand the trend of self-regulated learning research.
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.
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 | 0.010 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.131 | 0.174 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".