Connected by emotion: Teacher agency in an online science education course during <scp>COVID</scp>‐19
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.032 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it