Comparative Examination of the Primary School Science Curricula in Turkey (Curricula of 1992, 2001, 2005, 2013 and 2017)
Bibliographic record
Abstract
In the present study, the science course curricula of 1992, 2001, 2005, 2013 and 2017 taught at the primary school level in Turkey have been examined comparatively in terms of four main elements (Target, Content, Educational Status, and Measurement and Evaluation). The reason for investigating the science curriculum at five key years was to identify and examine main changes in the system. To this end, the main elements of the curricula were presented in a table. The similarities and differences between the curricula in question were determined and interpreted as a result of the evaluations. The study was carried out using the document review technique, among qualitative research methods. The science curricula published by the Ministry of National Education were analysed in the study. The curricula of the afore-mentioned years were first analysed separately in the process, and then the results were re-investigated by being gathered together. Therefore, it was attempted to ensure the consistency of the data. The results achieved show that the curricula cannot be realized as expected due to certain problems encountered in the process of implementation despite overall positive developments (the fact that teachers are not informed sufficiently, infrastructure problems, crowded classes, the lack of technological equipment, etc.)
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".