Comparative Analysis of Science Education Systems of Turkey and Canada
Bibliographic record
Abstract
Bu arastirma Turkiye ve Kanada (Ontario) fen egitimini ve ilkogretim okullari II. Kademe‟de uygulanan fen programlari arasinda farklar olup olmadigini arastirmak, farkliliklari ve benzerlikleri ortaya cikarmak icin karsilastirma yapmak amaciyla yapilmistir. Her iki ulke fen egitim sistemleri karsilastirilirken Turkiye ve Kanada (Ontario) fen ve teknoloji egitimi “amaclar” acisindan karsilastirmali olarak degerlendirilmis, Turkiye 2005 Fen ve Teknoloji Dersi Ogretim Programi (FTDOP ) ve Ontario, 1998 Fen ve Teknoloji Mufredati (OFTM) yapi, ogrenme alanlari ve ogretilmesi hedeflenen uniteler ve ogretilmesi hedeflenen unitelerden Isik (FTDOP) ve Optik (OFTM) unitesinin okutuldugu sinif, unite icerigi ve ogrenci kazanimlari acisindan benzerlik ve farkliliklari ortaya konulmustur. FTDOP ve OFTM arasinda yapilan karsilastirma neticesinde FTDOP‟nin 412 sayfalik bir dokuman olmasi, OFTM‟nin ise 110 sayfadan olusmasi ve FTDOP nin OFTM programina gore daha ayrintili olmasi dikkati ceken ilk farkliliklardir. FTDOP yedi ogrenme alanindan, OFTM ise bes ogrenme alanindan olusmaktadir. FTDOP‟nda sarmallik ilkesi benimsenmisken, OFTM‟nda ise bu ilke gozetilmemistir. Isik unitesinin bulundugu sinif seviyesi, ogrenme alanlari ve ogrenci kazanim sayisi bakimindan da her iki program arasinda farkliliklar mevcuttur. Bu farkliliklarin yaninda; FTDOP‟nda yapilandirici ogrenme yaklasiminin esas olarak alinmasi, ogrenci merkezli ogretiminin savunulmasi, programinin vizyonunun, fen ve teknoloji okuryazarligi olarak belirlenmesi, bilimsel surec becerilerini ve Bilim-Teknoloji- Toplum-Cevre iliskisinin on plana cikarilmasi, ogretimde bilgi ve iletisim teknolojilerinin kullanilmasinin onerilmesi, ogrenci cesitliliginin dikkate alinmasi yonunden OFTM ile paralellik gosterdigi gorulmustur.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.007 | 0.016 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".