Connected by emotion: Teacher agency in an online science education course during <scp>COVID</scp>‐19
Bibliographic record
Abstract
Abstract Taking on an agentic perspective, this study employed a digital ethnographic approach to examine a science teacher's emotional experiences in an online graduate science education course during the COVID‐19 pandemic. Veronika, the teacher, revealed her feelings of grievance and loss to the graduate course cohort at the advent of large‐scale school closures. Her emotions, shared through the online course, connected the members of the cohort to overcome emotional and pedagogical difficulties caused by the pandemic. She received both emotional and professional support from the cohort and designed an environmental related learning activity that centered on fun and connection in science learning. The activity stimulated students’ positive emotions and simultaneously served to reset Veronika's emotions. This study underlined that emotions connect teachers during a social crisis in ways that address obstacles encountered in teaching and learning. Lessons for teacher education include providing space for and acknowledging emotions in teaching, especially in times of stress and the importance of fostering agentic actions, collegiality, and collaboration by explicitly connecting an individual's emotions and beliefs to their professional practice.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".