Digital Leadership and Teacher Digital Competence as Keys to Successful Integration of Digital Culture in Education
Bibliographic record
Abstract
This study investigates the critical role of digital leadership and teacher digital competence in the successful integration of digital culture in education. Previous research has not sufficiently explored the correlation between digital leadership and teacher competence, particularly in the educational context of Southeast Asia, where these elements have remained understudied. The research was conducted at SMAN 1 Kutacane, Southeast Aceh, Indonesia, analyzing the influence of digital leadership and teachers' digital competence on the integration of digital culture. Using a mixed method, this study combines qualitative and quantitative approaches. Data were collected through surveys of 100 teachers and 50 students and in-depth interviews with 10 teachers. The results of the analysis show that digital leadership has a positive effect on teachers' digital competence, which in turn promotes the effective integration of digital culture in the educational environment. The findings of this study emphasize the importance of digital leadership for educational policymakers and school administrators, suggesting the need for targeted teacher competence development programs. Therefore, the findings are expected to guide the design of effective strategies for integration digital culture.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".