PISA 2006 Uluslararası Öğrenci Değerlendirme Programı'nda insan kaynakları ve fiziksel kaynakların öğrencilerin fen okuryazarlığına olan etkisinin kültürlerarası karşılaştırılması.
Bibliographic record
Abstract
This study investigates the students’ characteristics and school characteristics and their influences on scientific literacy skills of 15-year-old students across Turkey, Canada, and Sweden, through the use of data from Organisation for Economic Co-operation and Development’s (OECD’s) Programme for International Student Assessment (PISA) 2006. The purpose of this study is to gain a more complete understanding of the effect of human and physical resource allocations and their interaction on students’ scientific literacy skills using Hierarchical Linear Modeling (HLM) techniques. By PISA 2006 results, in terms of scientific literacy, Canada is a high performing country whereas Turkey is far below the average and Sweden has a rank in the average. For modeling scientific literacy, student-level characteristics determined by student questionnaire, and school-level characteristics determined by school questionnaire were used. Results of the present study indicated that there were significant between-school differences in scientific literacy skills of students for all three countries. Turkey had the highest between-school variance and it was more than half of the total variance whereas in Canada and Sweden they were far lower. School type and size were common school factors affecting students’ scientific literacy skills in Canada and Sweden; however, in Turkey school admittance policies, educational resources, science promotional activities, and teacher qualities were school characteristics which have impact on scientific literacy. Enjoyment of learning science, self-efficacy in science, general value given to science, awareness of environmental issues, responsibility for sustainable development, and confidence in use of information technologies were common student factors affecting development of scientific literacy skills in the three countries. Finally, in all three countries cross-level interactions of student and school characteristics for developing scientific literacy skills were observed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.004 | 0.005 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.009 | 0.004 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.015 | 0.020 |
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; both teacher heads agree on what is shown here.
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".