Empowering Teacher Leadership Through Collaborative Networks for Lifelong Learning in Myanmar’s Higher Education Institutions
Bibliographic record
Abstract
This qualitative study investigates the teacher leadership role in promoting lifelong learning in Myanmar's higher education institutions. By focusing on teacher competencies and collaborative networks empowering teacher leadership, twelve participants from Higher Education Institutions across Myanmar and offices of the Department of Higher Education (DHE) were interviewed. The relevant articles and policy documents were used to form semi-structured interviews and principles of thematic analysis for this research. The study stated that the current situation for collaborative networks promoting lifelong learning in Myanmar was insignificantly supported. The application of professional learning communities (PLCs) rarely enhanced leadership, which was one of the key competencies for teachers. The findings highlighted the possibility of recommendations, which were 1) supporting teacher leadership, 2) institutional support, 3) applying global guidelines, 4) providing technology needs, and 5) implementing PLC practices effectively in Myanmar’s context. Further research should focus on collaborative networks among teachers through practical workshops empowering leadership and teacher competency development.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".