A Comparative Analysis of the Science Curricula Applied in Turkey Between 2000 and 2017
Bibliographic record
Abstract
This study aims to conduct a multidimensional analysis of the 2017 Science Curriculum taking the previous three curricula into account. The document analysis technique, which is a qualitative research method, was used. The Science curricula of 2000, 2005, 2013 and 2017 were analyzed in detail for this purpose. The 2017 Science Curriculum, which is one of the last four curricula, was described and interpreted by discussing its important qualities as well as the similarities and differences between this curriculum and the other curricula. In addition, it was found out that the Science curricula used between 2000 and 2017 were in line with the Ohio Competency-Based Science Model. The skills included in these four curricula, the contents used to have skills acquired, the materials used to equip students with the skills, and the conditions where the acquisitions are expected to be used are pointed out in this study taking into account the key elements included in the Competency-Based Science Model. We think that the findings of this study are important as they reveal the general points of view and consistency of the curricula rather than showing their superiorities or shortcomings in relation to each other.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| 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".