Transformative learning: reflection on the emotional experiences of schoolteachers during and after the pandemic
Bibliographic record
Abstract
Teachers have experienced online teaching anxiety since the pandemic, and as education continues with digitization, the emotional experiences should be addressed. By focusing on the emotions experienced by schoolteachers in online teaching, this research investigates how intense feelings, and strong emotions can be transformed into critical self-reflection and ultimately achieve transformation based on the transformative learning model. As teachers across jurisdictions reportedly experienced burnout, this research discovers that transformative learning is the gateway and a path that allows teachers' passion to be reignited. To cope with the changes and challenges brought by the use of AI and the vastness of online information, it is essential for teachers to re-examine and identify their roles in the classroom and to consolidate their valuable contributions and irreplaceable role in an effective learning environment. Through case studies that cover the life stories of five teachers in Hong Kong, Canada and Taiwan, this research discusses how the emotionality of teachers plays a key role in transformative learning and examines the process in which anxieties transcend into passion.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.023 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.004 | 0.011 |
| Insufficient payload (model declined to judge) | 0.003 | 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".