Utilization of government grants for funding: insights into STEM education teachers in Taiwan
Bibliographic record
Abstract
Over the past three years, Taiwan’s Ministry of Education has established 1300 classrooms for science, technology, engineering, and mathematics (STEM) education nationwide, providing unprecedented budgetary support for the procurement of STEM equipment in junior high schools. This study examines how STEM teachers utilize government grants for equipment procurement and classroom setup, with a particular focus on the key factors that influence their decision-making processes. This study employs descriptive statistics, logistic regression, and multiple-choice analysis of data obtained through surveys of 75 science and technology teachers and expert opinions. The results reveal that teachers have adopted a cautious approach emphasizing simplicity and safey, prioritizing convenience, practicality, and versatility in terms of equipment choices. The logistic regression results indicated a significant correlation between perceived importance and purchasing decisions (ratio = 3.654, explained variance = 23.7%). Multiple-choice analysis found a skewed emphasis on curriculum indicators. The study develops a benchmark table for facilities and equipment, offering insights into resource optimization, educational equality, interdisciplinary integration, and teacher training. Acknowledging the limitations, including sample size constraints and potential biases, the findings serve as a valuable reference for educators and encourage budget adjustments aligned with curriculum guidelines.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 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".