Relationships between Students’ Socioeconomic Status, Parental Support, Students’ Hindering, Teachers’ Hindering and Students’ Literacy Scores: PISA 2018
Bibliographic record
Abstract
This research was conducted in Turkey and it examined the relationships between fifteen-year-old students’ PISA 2018 literacy scores and student-level and school-level variables. This study aimed to examine the relationships between students’ hindering, teachers’ hindering, socioeconomic status, parental support and student achievement. The research is a correlational study. A relational screening model was used in this research. Six thousand eight hundred and ninety students from one hundred and eighty-six schools in twelve regions of the Nomenclature of Territorial Units for Statistics (NUTS) Level 1 joined PISA 2018 in Turkey. OECD selected these students randomly. In PISA 2018, school sampling was determined by a stratified random sampling method. Teachers’ hindering, students’ hindering questionnaires are from the school principals’ questionnaire and the parental support questionnaire was taken from the student questionnaire. Additionally, students’ genders and their socioeconomic status were taken from the background questionnaire. To analyze these variables, a hierarchic linear model was used. Multilevel structural modeling (MSEM) was selected and Bayesian estimation with latent variables was performed. There are direct relationships between students’ genders, socioeconomic status, teachers’ hindering behaviors, students’ hindering behaviors, parental support and Turkish students’ reading skill scores. There is also an indirect relationship between teachers’ hindering behavior and students’ reading skill scores via students’ hindering behavior.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".