The Usage Level of Time-Saving Measurement and Evaluation Techniques in Teacher Training Programs
Bibliographic record
Abstract
The aim of this study is to explore the level of use of timesaving measurement and evaluation techniques in pre-service teacher training. The research is designed and conducted as a descriptive survey. 200 teacher candidates studying in seven different teacher education programs conveniently sampled from the education faculty located in the Mediterranean region of Turkey. The data was collected through the inventory developed by the researcher. The data analyzed with pairwise and multiple comparison techniques. The study revealed that the instructors at the education faculty were using time-saving measurement and evaluation techniques in their courses at a moderate level. These techniques were mostly used the by pre-school education, and least used in the department of mathematics education. The most commonly used time-saving measurement and evaluation technique in all teacher training programs was the Advantage/Disadvantage Listing Technique. The least-used technique was One Minute Paper Test. The prospective teachers’ opinions did not differ according to their gender. Findings have been discussed in terms of teacher qualifications on the relative to current literature.
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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 | 0.005 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".