Development of Leadership Indicators and Approaches for the District Directors of Non-formal and Informal Education in the Digital Era
Bibliographic record
Abstract
The development of leadership indicators and approaches for district directors of non-formal and informal education in the digital era is crucial for adapting to technological advancements, enhancing leadership effectiveness, fostering creativity and innovation, promoting digital citizenship, and supporting ongoing professional development. These efforts ultimately contribute to the improvement of educational practices and outcomes in the digital age. The objectives of this research were as follows: To study the components and indicators of leadership among district directors of non-formal and informal education in the digital era. To examine the consistency of the developed leadership indicator structure model for district directors of non-formal and informal education in the digital era using empirical data. To develop a leadership development approach for district directors of non-formal and informal education in the digital era. To study the results of implementing the leadership development approach for district directors of non-formal and informal education in the digital era. The research was conducted in four phases, and statistical analysis included percentage, mean, standard deviation, and Confirmatory Factor Analysis. The results revealed the following: Leadership among district directors of non-formal and informal education in the digital era consisted of five components and fifteen indicators. The leadership development approach for district directors of non-formal and informal education in the digital era included the following components: creativity, participation, digital citizenship, digital vision, and digital professionalism. These components were prioritized based on the findings. The assessment of satisfaction with the leadership development approach for directors of district centers for non-formal and informal education in the digital era indicated a high level of overall satisfaction. These innovative approaches function as catalysts, not only propelling district directors to excel in their roles but also empowering them to champion innovation within the education domain. Most importantly, they play a pivotal role in driving substantial progress in the field of education as our world becomes increasingly digital.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Other design | high |
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".