An examination of educational inputs with the data envelopment analysis: The example of ICILS 2013
Bibliographic record
Abstract
The aim of this study was to determine how efficiently different countries, comparatively, use educational inputs, which are considered to affect information and communication technology literacy. The study was designed using the survey model. The study was conducted with data belonging to 21 countries participating in the International Computer and Information Literacy Study (ICIL) 2013. The data of this study were grouped as educational inputs and educational outputs. The educational inputs were the ratio of school size and teachers, the ratio of school size and number of computers, the ratio of school size and number of computers available for students, the ratio of school size and number of computers with access to internet/World Wide Web, and the ratio of school size and number of smartboards. The educational outputs were determined by the average student grades obtained in ICILS 2013. The data were analysed with data envelopment analysis. The research results revealed that relatively, Australia, Canada (Newfoundland and Labrador, Ontario), Denmark, Korea, and Norway were the countries with total efficiencies. It was determined that with the exception of the Czech Republic, all the countries without total efficiencies had the characteristic of increasing returns to scale. According to the projections that were put forward for countries to become totally efficient, the most reduction recommendations were received for the inputs for ratio of school size and teachers by Argentina (Buenos Aires); for ratio of school size and number of computers, ratio of school size and number of computers available for students, and ratio of school size and number of computers with access to internet/World Wide Web by Turkey; and for ratio of school size and smartboards by Thailand. That is to say, these countries were the ones least able to use these inputs efficiently.
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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 | 0.008 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".