Comparative Study: Impact of Family, School, and Students Factors on Students Achievements in Reading in Developed (Estonia) and Developing (Azerbaijan) Countries.
Bibliographic record
Abstract
The work is based on Pisa 2009 International Assessment Study. Two counties were selected: a developed country, Estonia and a developing country, Azerbaijan. The following Datum was used for statistical analysis: students average scores in reading (162 schools, 4 600 students from Azerbaijan; 17 schools, 4 923 students from Estonia). The work is based on mixed type research. The main goal is to determine the effect of different independent variables related to school, student, and household, on students’ achievements in reading in developed and developing countries. Three research questions were developed: 1) To what extent are student background variables associated with reading scores in each country? 2) To what extent are family-related variables associated with reading scores in each country? 3) To what extent are school-related variables associated with reading scores in each country (variables were analyzed separately: organizational; instructional; and teacher-related)? As with other research studies this research may have some limitations regarding research model, data analyses, and interpretations. For data analyses ttest and General Linear Model – Univariate statistics were applied. General Findings: Estonia: Most of the teachers apply modern teaching methods; students’ attitude towards reading activities, teacher, and school are positive. Parental involvement has a positive effect on students’ achievements in reading. Azerbaijan: The majority of the teachers do not apply modern teaching methods; students’ attitudes towards reading activities are not deemed positive; students have negative attitudes towards school and teachers.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".