The Effect of School Capacity and Teacher Capacity on Student Experience in Using Digital Devices: A Comparative Study in Thailand and Taiwan
Bibliographic record
Abstract
This study aimed to compare the levels and relationships between school capacity, teacher capacity, and student experience in Thailand and Taiwan. Moreover, school types and school locations were examined to explore these differences. The secondary data from PISA 2018 were analyzed by quantitative approach. The results revealed that only school types had differences in teacher capacity in Thailand, whereas other demographic information did not. Other results showed different directions of correlations between school capacity, teacher capacity, and student experience. School capacity in both countries was able to predict teacher capacity. Other factors as predictors of the student experience are recommended. Keywords: comparative study, digital learning, school capacity, student experience, teacher capacity Cette étude visait à comparer les niveaux et les relations entre la capacité des écoles, la capacité des enseignants et l'expérience des étudiants en Thaïlande et à Taïwan. En outre, les types d'écoles et les lieux d'enseignement ont été examinés pour explorer ces différences. Les données secondaires de PISA 2018 ont été analysées en employant une approche quantitative. Les résultats ont révélé que seuls les types d'écoles étaient associés aux différences dans la capacité des enseignants en Thaïlande, alors que les autres informations démographiques ne l’étaient pas. D'autres résultats ont montré différents types de corrélations entre la capacité des écoles, la capacité des enseignants et l'expérience des élèves. Dans les deux pays, la capacité des écoles a permis de prédire la capacité des enseignants. Il est recommandé d'utiliser d'autres facteurs pour prédire l'expérience des élèves. Mots clés : étude comparative, apprentissage numérique, capacité de l'école, expérience de l'élève, capacité de l'enseignant
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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".