Effective Social-emotional Learning Strategies in the Online University Classroom
Bibliographic record
Abstract
With the increasing prevalence of online education, especially since the pandemic, there is a growing need to explore effective strategies for fostering social-emotional learning (SEL) in virtual university classrooms. The key components of SEL are self-awareness, self-management, social awareness, relationship skills, and responsible decision-making. In this study, the researcher explores effective SEL strategies in online university classrooms. Although researchers have widely investigated the topic of SEL programs in K-12 settings, few have focused on SEL strategies in adult-level or university settings, specifically for online environments. In this qualitative, phenomenological study, the researcher investigates professor perceptions regarding effective SEL strategies in university online environments. Participants in the study are professors with experience teaching at the graduate or undergraduate level in a private university in Tennessee. The researcher collects data from a questionnaire, semi-structured interviews, and a focus group discussion. Several strategies emerge as effective in promoting SEL, including sympathy building, supportive environment, and building connections. By implementing these strategies, educators can effectively promote SEL in online university classrooms, thereby enhancing the overall well-being of students, academic success, and readiness for the online environment. However, further research is warranted on the implementation of these strategies in diverse online learning environments.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".