Characteristics of 15-Year-Old Students Predicting Scientific Literacy Skills in Turkey
Bibliographic record
Abstract
<p class="apa">Since 2003, Turkey regularly participates in PISA. According to the PISA 2012 results, 15-year-old students in Turkey performed below both OECD countries and participating countries. Defining the relations between students’ characteristics and their scientific literacy skills is thought to provide deeper understanding for the nature of this situation in Turkey. The main aim of this study was to construct a significant multivariate model with secondary level structural equating modelling which includes relations between students’ characteristics and their scientific literacy performance by using PISA 2012 Turkey data. Also, according to this model, it was aimed to define and interpret the predictive level of these characteristics to the scientific literacy skills of students. This study was designed as a basic research and secondary level analyses were conducted on PISA 2012 Turkey student questionnaire data. PISA 2012 Turkey sample was composed of 4.848 students. A secondary-level structural model was constructed by using PISA data. Limitations of the model, best predictor of scientific literacy skills were ‘socio-economic status’. Students’ ‘opinions for teacher’ shows negative correlation with scientific literacy skills. Students’ ‘attitudes for school’ have low but positive correlation with scientific literacy skills. Among indicators, best predictor of scientific literacy skills is ‘home possessions’. It is followed by ‘index of economic, social and cultural status’ and ‘wealth’. Lowest predictors among indicators are ‘attitude towards school: learning outcomes’, ‘attitude towards school: learning activities’ and ‘sense of belonging to school’ respectively. All these variables are positively correlated with scientific literacy skills.</p>
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".