Guidelines for Implementing the Ministry of Education's Digital Action Plan of Educational Institutions under the Foundation of the Church of Christ in Thailand
Bibliographic record
Abstract
The objective of this research is to study 1) Implementing the Ministry of Education's Digital Action Plan of educational institutions, and 2) Guidelines for Implementing the Ministry of Education's Digital Action Plan of educational institutions under the Foundation of the Church of Christ in Thailand. The research was mixed method. The population was 28 institutions of educational institutions under the Foundation of the Church of Christ in Thailand by purposive sampling. Informants were 84 people including the head of an educational institution, the assistant of the head of educational institutions, and the head staff of a learning group. Research instruments were interviews and rating scales. Qualitative data analysis by content analysis, quantitative data analysis by average, and standard deviation, and present the data by descriptive analysis. The results found that 1) Implementing the Ministry of Education's Digital Action Plan of educational institutions under the Foundation of the Church of Christ in Thailand was the highest level. When considered by aspect, it was found that the highest average was enhancing knowledge and understanding of the action plan, and developing a process for the plan/project. 2) Guidelines for Implementing the Ministry of Education's Digital Action Plan of educational institutions under the Foundation of the Church of Christ in Thailand where executives allocate budget in line with the plan, support the support for effective implementation of the plan, supervise for preparation of the plan, apply technology to follow up the performance, prioritize plan/project, and development 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 | 0.026 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".