An Analysis of the Studies Conducted in the Field of Curriculum Evaluation from a “Methodology” Perspective
Bibliographic record
Abstract
This study analyzed studies done in Turkey in the context of curriculum evaluation (CE) by asking, “How is it made? The study was carried out in two stages. In the first stage, the document analysis method used 215 theses written between 1991 and 2020 on CE were analyzed according to the “thesis review form.” In the second stage, depth analysis was made through semi-structured interviews with the authors (students) and the field experts (supervisors of the authors) of the theses to make the results of the first stage more understandable. Interviews were conducted with 32 participants. A maximum sampling method was used to determine the participants. The data analysis calculated percentage and frequency values for the data obtained in the first stage. In the second stage, descriptive analysis and content analysis were carried out with the MAXQDA 2020 qualitative data analysis program. The majority of theses did not employ a CE model as a consequence of the research, and the CIPP model was the most popular CE model. Many of the theses were not justified in using the CE model. Model usage increased as time passed to the present day. Many theses used quantitative models but did not explicitly state the sampling technique. Teachers were mainly used in this research as a source for data gathering, and participant numbers ranged from 10 to 50. Additionally, most studies used questionnaires and interviews as the primary data-gathering tools. All of these findings suggest that CE studies have several flaws.
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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 | 0.028 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.018 | 0.022 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| 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".