Engaging Students in Social Emotional Learning During the COVID-19 Pandemic: The Lived Experience of Three High School Teachers in the United States
Bibliographic record
Abstract
Although Social Emotional Learning (SEL) is recommended for grades K-12, research suggests that what is effective in elementary and middle schools—having a separate SEL curriculum—is less effective in high schools (Yeager, 2017). Instead, engaging high school students in SEL through pedagogic practice and the subject area curriculum is encouraged. To do this, high school teachers need SEL instruction and supports, but report few available opportunities (Hamilton et al., 2019). Additionally, few SEL studies exist in the secondary context to help guide high school teachers, and the COVID-19 pandemic further emphasized the need for SEL. To begin to address this gap in SEL research, a series of classroom observations and interviews were conducted to better understand three high school teachers’ lived experiences of SEL. Using an approach inspired by Max van Manen’s (2016) hermeneutic phenomenology, a common theme emerged. The teachers all identified adapting the pace of curriculum during the COVID-19 pandemic as a phenomenon that inherently engaged students in SEL. The implications of this finding for teacher education and professional learning are considered.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.019 | 0.011 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.007 |
| 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".