The Leaders of Digital Education Transformation
Bibliographic record
Abstract
The educational reform in Thailand is currently in the era of Education 4.0. From the perspective of the concept of modern educators, this completely stimulates the role of educators and practitioners. With the rapid adaptation to the digital age, this is a new education dimension that requires application process. Digital technology contributes to a rapid transition due to the spread of the COVID-19 pandemic, it leads to the immediate adaptation to all sectors to accommodate the circumstances. From the past, it is not only education that has had to adapt rapidly. Undeniably, technology serves as a key of development, not only advancing science, engineering, and industry by creating tools and conveniences but also by enhancing human potential and improving the quality of life. Therefore, educational development is extremely important to ensure that educators possess the knowledge, awareness, and ability to lead in the rapid advancement of technology. The challenge of being a leader in educational transformation in the digital age is that adapting to various technologies is something that must be carefully considered how it should be approached and in which direction. Additionally, it is crucial to understand what knowledge is necessary for effective adaptation. Most importantly, the approach to being a leader in digital educational transformation requires a deep understanding of what must be done. This issue should be given significant attention because the key to successful transformation lies with the leader. If the leader is not one who drives change, the transition to the modern era will not happen. Therefore, the competencies required for being a leader in Digital Transformation encompass several important aspects. In other professional fields, Digital Literacy, Digital Readiness, Digital Culture, Digital Mindset in particular, Digital Leadership who need cooperation, communication, help and mutual support in the organization.
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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 | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.008 |
| Scholarly communication | 0.014 | 0.010 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.003 | 0.010 |
| Insufficient payload (model declined to judge) | 0.016 | 0.005 |
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".