A Global Bibliometric Analysis of Assessment Literacy in Preservice Teacher Education (2000–2024)
Bibliographic record
Abstract
This study conducts a bibliometric analysis of assessment literacy research in preservice teacher education from 2000 to 2024.The analysis, based on 91 peer-reviewed journal articles indexed in the Scopus database, examined publication patterns, thematic groups, geographical location, and most-cited works using VOSviewer for co-occurrence mapping and cluster visualization.The findings showed that publications on assessment literacy rose sharply in the early 2020s, peaking in 2023.Cluster analysis identified three thematic areas: the development of teacher competencies and professional skills, connections between assessment literacy and instructional practices, and preservice teacher preparation in technological and pedagogical means for effective assessment.Most studies were conducted in the United States, followed by Australia and Canada, whereas in other regions, there were only a few representations.Popham's ( 2009) study on policy alignment, innovative pedagogy, and technology use emerged as the most influential work.These findings highlight the need to diversify geographical contributions, broaden methodological approaches, and strengthen crosscontextual dialogue in literacy assessments.By synthesizing trends across more than two decades, this study offers the systematic bibliometric mapping of preservice teacher education in assessment literacy, addressing gaps left by prior reviews.These findings imply that future research should aim to refine the conceptual and practical foundations of assessment literacy in preservice teacher education.The study provides a foundation for future global collaborations and informs curriculum design in teacher education programs.
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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 | 0.009 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.195 | 0.223 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.003 |
| 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".