History Teaching Approaches Preferred by Turkish and Russian History Teachers
Bibliographic record
Abstract
The purpose of this research is to determine the opinions of Turkish and Russian history teachers regarding teaching of history, and to present, on a comparative basis, the understanding of history in the two countries as well as the methods of history teaching preferred by the teachers there. The research was designed as a case study, which is one of the qualitative research methods. The sample consisted of 13 Turkish and 13 Russian teachers working as history teachers in Turkey and Russia in the 2020- 2021 academic year. The convenience sampling method was used in the study. The data of the study were collected by correspondence via e-mail with a questionnaire form consisting of open-ended questions created by the researchers. Descriptive analysis was used to analyze the data. When the results of the study are evaluated in general, it is observed that the Russian history teachers are more flexible in history teaching and attach more importance to innovative history teaching, while the Turkish teachers perform more curriculum-centered history teaching compared to their Russian colleagues. In addition, it can be said that Russian history teachers pay more attention to their professional development than Turkish history teachers, and they incorporate more historical thinking skills in classroom activities. It is possible to say that the results of the study originate from the objectives of history teaching in the two countries.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".