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Record W4387883974 · doi:10.23977/aetp.2023.071407

PBL's Analysis on Embedding Social and Emotional Learning in College Classrooms

2023· article· en· W4387883974 on OpenAlexvenueno aff
Minmin Zhang

Bibliographic record

VenueAdvances in Educational Technology and Psychology · 2023
Typearticle
Languageen
FieldPsychology
TopicEmotional Intelligence and Performance
Canadian institutionsnot available
Fundersnot available
KeywordsSocial emotional learningEmotional competencePsychologySocial competenceCurriculumCompetence (human resources)Mathematics educationSocial skillsProject-based learningCooperative learningEmotional intelligencePedagogyTeaching methodSocial psychologySocial changeDevelopmental psychology

Abstract

fetched live from OpenAlex

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.

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.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.268
Threshold uncertainty score0.823

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.003
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.034
GPT teacher head0.436
Teacher spread0.402 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

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