Fostering Reflective Educators: The Transformative Impact of TATAL Communities of Practice
Bibliographic record
Abstract
Introduction: Talking About Teaching and Learning (TATAL) workshops serve as inclusive communities of practice (CoPs), offering educators a platform to explore their teaching philosophies and practices collaboratively. Despite anecdotal evidence of TATAL’s effectiveness, limited research explores its long-term impact on teaching practices and academic resilience. Research Aim: How do TATAL workshops influence educators' personal and professional growth, teaching philosophies, and resilience? Methodology: This study analyzes qualitative and quantitative data collected from 876 participants across 25 TATAL workshops between 2015 and 2021. Using thematic analysis and the interpretive lens of sense-making, the study identifies key themes and subthemes that characterize participants’ experiences. Results: Findings reveal three primary subthemes: (1) Partnered Learning Communities fostering collaboration and shared reflection, (2) Practical Tools and Techniques enhancing teaching practices, and (3) Transformational Learning experiences facilitating personal and professional growth. Participants reported increased clarity of teaching philosophies and increased resilience in educational contexts. Discussion: TATAL workshops disrupt traditional power dynamics in education, offering a space for equitable, reflective, and collaborative learning. This study emphasizes TATAL’s role in fostering reflective educators and building institutional capacity. Conclusion: TATAL represents a transformative professional development model. Its collaborative and reflective approach holds significant implications for higher education institutions seeking to enhance teaching quality and academic resilience.
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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 | 0.016 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.012 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.021 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".