Quality of Educational Resources: A Comparative Evaluation of Schools That Joined PISA 2015 from Turkey and Singapore
Bibliographic record
Abstract
The objective of the study was to make a comparison among the two countries according to the level of sufficiency of educational resources and to determine the accuracy level at which variables related to educational resources can classify the schools on the basis of countries. Relational survey model was used. The sample group of the study was comprised of 186 schools from Turkey and 174 schools from Singapore for a total of 360 schools. Descriptive analyses and chi-square statistics were used to put forth whether there are differences with regard to the items. Logistic regression analysis was used to make an accurate classification of the schools according to their countries. Statistically significant differences between Turkish and Singapore schools were attained as a result of the chi-square analyses in all variables including lack of educational material, inadequate or poor quality educational material, lack of physical infrastructure and inadequate or poor quality physical infrastructure variables. A total of four variables included in the study explain about 60 % of the variance of Turkish and Singapore schools in having adequate educational resources. The equation obtained from analysis shows that lack of educational material is more important than lack of physical infrastructure. This alone puts forth that school success is related more to the quality of educational material than to physical inadequacies. As a result of the logistic regression with these variables, it was determined that the equation classifies 82% of the total number of 360 schools accurately. As a general conclusion of the study, it was observed with regard to its contributions to the model acquired via logistic regression.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".