PBL's Analysis on Embedding Social and Emotional Learning in College Classrooms
Bibliographic record
Abstract
The classroom environment is an important place to effectively implement social and emotional education. College students' social and emotional competence can be improved by embedding social and emotional learning in curriculum learning. Among them, setting up special social and emotional courses or integrating social and emotional competence into subject teaching are effective embedding methods. Project-based learning, which combines learning interaction and cooperative learning, is an effective way to improve the competence to embed social emotions in the classroom. In the whole process, project-based learning constructs a triple relationship between students and themselves, students and others, and students and the collective, which can be highly integrated with the five social-emotional abilities and skills. However, in the process of project-based learning, a more detailed design is needed in the stages of project selection, project process, project completion, and project evaluation. The conclusion of this study provides useful enlightenment on how to improve a college student's social and emotional competence in the campus environment.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".