COMPARISON OF THE TURKISH LANGUAGE TEACHING PROGRAM WITH THE MOTHER LANGUAGE TEACHING PROGRAMS OF THE CONTINENTAL OF PISA 2018
Bibliographic record
Abstract
Various national exams are administered in Turkey to evaluate academic achievement and identify areas in need of improvement. In addition to these national assessments, Turkey also participates in several global evaluation projects to determine its position in the international education arena. One of these initiatives is the Programme for International Student Assessment (PISA), conducted by the Organisation for Economic Co-operation and Development (OECD). PISA evaluates students' reading skills as well as their literacy in mathematics and science. This study comparatively examines the mother tongue curricula of countries that ranked first in reading skills on each continent according to the 2018 PISA results, alongside the Turkish Language Curriculum implemented in Turkey. In this context, the mother tongue curricula of South Korea (Asia), Estonia (Europe), Australia (Australia), Chile (South America), Canada (North America), and New Zealand (Oceania) were analyzed. These curricula were evaluated based on key components such as goals, instructional practices, content, assessment methods, and grade levels. The similarities and differences between the programs were identified. Data for the research were gathered by translating documents obtained from the official websites of the ministries of education of the respective countries. Since grade levels vary among countries, no restriction was applied to a specific grade level during the comparison process. The findings indicate that there are similarities in the outcome-oriented structure of mother tongue curricula. However, significant differences exist in terms of the supporting skills, values, instructional materials, teaching methods and techniques, and assessment approaches incorporated within these programs. These differences are discussed in detail in the conclusion section, along with various suggestions for updating the Turkish Language Curriculum.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".