Adaptation of ATI-R Scale to Turkish Samples: Validity and Reliability Analyses
Bibliographic record
Abstract
Teachers’ teaching approaches have become an important issue in the search of quality in education and teaching because of their effect on students’ learning. Improvements in teachers’ knowledge and awareness of their own teaching approaches enable them to adopt teaching process in accordance with their students’ learning styles. The Approaches to Teaching Inventory (ATI-R), which has been developed and revised in this framework, is a scale which is effectively used to define teaching approaches in different cultures. Originally written in English, the ATI-R’s validity and reliability results were very positive. The scale’s validity and reliability analyses in different languages and cultures have produced a wide range of different results. The aim of this paper is to adapt the scale in the Turkish language. Firstly, in order to handle linguistic equivalence, data collected from 40 teachers were analyzed, and then for confirmatory and reliability analyses data were collected from 485 teachers. According to the analyses, the scale has two dimensions, and under these two dimensions there are four sub-factors. Reliability and validity results in Turkish culture are also acceptable. As a result, the scale can be administered to define teachers’ teaching approaches in Turkish samples.
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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 | 0.011 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".