Exploring Trends in Education Program Evaluation Models in High Schools: Bibliometric Analysis
Bibliographic record
Abstract
This study aims to analyze the program evaluation model applied in high school using a bibliometric approach integrated with interpretive analysis. The main problem identified was the importance of understanding the various evaluation models that are often applied in education and their influence on improving the quality of the program. The method used in this study is a bibliometric analysis by selecting 120 articles published between 1985 and 2024 from the Scopus database related to the evaluation of educational programs in secondary schools. Data was collected through a search for articles with relevant keywords, and an analysis was conducted to find thematic trends and collaborations between authors in this field. Data were analyzed using the prism method and the R Studio application with the Bibliometrix::biblioshiny() package to explore thematic trends, author collaboration networks, and conceptual structures. The results show that evaluation models, such as the Outcome-Mediation Cascade (OMC), TADIPHE, and EP_PISTdu, are increasingly relevant in complex educational contexts. The study also found the importance of gender elements in program evaluation, with a focus on equality between women and men. In addition, international collaborations between authors from different countries, such as the United States and Canada, are instrumental in enriching the quality of program evaluation research. This study suggests that the development of more innovative and relevant evaluation models should be carried out to increase the effectiveness of educational program evaluations in the future.
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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 | 0.065 | 0.225 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.181 | 0.248 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.014 | 0.011 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".