Unpacking Professionalism and Capacity Building in Early Childhood Education: A Phenomenological Approach
Bibliographic record
Abstract
Backround: Professional development and capacity building are essential for improving quality, equity, and outcomes in early childhood education globally. As systems strive to enhance the skills and knowledge of educators, understanding the lived experiences of early childhood professionals is crucial for designing effective programs. Objective: This study aims to explore the experiences of 58 early childhood professionals from Australia, Canada, and Taiwan who participated in systematic professional development and capacity-building initiatives. The focus is on understanding how these initiatives shape educators' professional identities, growth, and pedagogical practices. Method: A phenomenological approach was employed, utilizing van Manen's hermeneutic phenomenological methodology. Data were collected over 14 months through in-depth semi-structured interviews, focus group discussions, professional portfolios, and reflective journals. Findings and Impications: The study identified four key themes: Conceptualizing Professionalism (navigating evolving professional identities), Capacity Building Strategies (mentoring, technology, and communities of practice), Barriers and Facilitators (time, funding, organizational support), and Transformation and Impact (improvements in pedagogy, leadership, and organizational culture). Conclusion: The findings highlight the importance of professional development in fostering identity transformation, critical reflection, and systemic change. Organizational culture, leadership support, and collaborative networks were crucial for effective capacity building. The study underscores gaps in mental health support and inclusive practices, offering insights for future professional development and policy frameworks in early childhood education.
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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 | 0.009 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.026 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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".