Investigation of Mathematics-Specific Trend Variables in PISA Studies with Neural Networks and Linear Regression
Bibliographic record
Abstract
This study aimed to examine the importance levels of mathematics-specific trend variables in PISA (Programme for International Student Assessment) 2003 and 2012 in predicting mathematics performance across years with a two-step analysis method. The sample of the study was 9703 Turkish students (N2003=4855 and N2012=4848) selected by clustered and systematic sampling methods. As data analysis methods, multilayer perceptron and radial basis functions techniques of artificial neural networks and multiple linear regression were used. In the two-step analysis, first, the least erroneous model was selected as the analysis method. Then, variable importance analysis was performed with this method. The results with the lowest relative errors were obtained by the multilayer perceptron when compared to radial basis functions. The results of neural network analysis had similar or lower error rates when compared to multiple linear regression. In both PISA cycles, significant predictors were mathematics self-efficacy, mathematics interest, student-teacher relations in school, attitudes towards the school, mathematics self-concept, mathematics instrumental motivation, and teacher support in mathematics classes, respectively. The results were discussed in the light of relevant literature.
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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 | 0.009 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 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".