Analyzing TALIS Indicators and PISA Results with Data Envelopment: Comparison of EMS, DEAP and R Software
Bibliographic record
Abstract
The Teaching and Learning International Survey (TALIS) and the Programme for International Student Assessment (PISA) are large-scale measurements about teaching and learning. There is a link between TALIS indicators and PISA results. We investigated which countries are effective according to TALIS indicators as inputs and PISA 2015 mathematics, scientific, and reading literacy scores as outputs in this research. Common 24 countries' data from TALIS 2013 and PISA 2015 were analyzed. Data envelopment analysis was used in this quantitative research. Belgium, Denmark, Finland, Italy, Korea, Mexico, Netherlands, Norway, and Portugal were found to be effective countries in EMS 1.3, DEAP-XP 2.1, and R-4.0.3 software according to the input-oriented CCR model. Belgium, Canada, Denmark, Estonia, Finland, Italy, Japan, Korea, Mexico, Netherlands, Norway, and Portugal were found to be effective countries in EMS 1.3, DEAP-XP 2.1, and R-4.0.3 software according to the input-oriented BCC model. The results obtained from the BCC and CCR model differ partially. Italy and Norway should be taken as reference the mostly by ineffective countries for getting better PISA score according to both models analyzing with EMS 1.3, DEAP-XP 2.1, and R-4.0.3.
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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 | 0.031 | 0.126 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.006 | 0.015 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".