Promoting Positive Emotions during the Emergency Remote Teaching of English for Academic Purposes: The Unexpected Role of the Constructionist Approach
Bibliographic record
Abstract
Despite the significant research on the effectiveness and challenges of emergency remote teaching (ERT) during the global COVID-19 pandemic, few studies have focused on the systematic facilitation of positive emotions by classroom teachers. This study aimed to identify the strategies that teachers of English for Academic Purposes (EAP) used during the ERT period, by interviewing 18 university English teachers in Hong Kong. Our results suggest that one traditional learning theory, the constructionist approach, played an unexpectedly important role in facilitating positive student emotions, as well as encouraging learning. Cognitively demanding tasks helped divert students’ attention away from the negative emotions they faced and towards their learning. Interactions also played an essential role in helping students learn and in reducing negative emotions. These results shed light on the significance of positive emotions in an online or ERT environment, with significant implications for university teachers who want their teaching to systematically promote positive emotions.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".