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Record W4404540141 · doi:10.5430/jct.v13n5p181

Didactic Strategy to Develop Socioemotional Competencies in University Students

2024· article· en· W4404540141 on OpenAlexvenueno aff
Mike Arthur Herrán Sifuentes, Alejandro Cruzata-Martínez, Miguel A. Saavedra-López, Ronald M. Hernández

Bibliographic record

VenueJournal of Curriculum and Teaching · 2024
Typearticle
Languageen
FieldComputer Science
TopicEducational Innovations and Technology
Canadian institutionsnot available
Fundersnot available
KeywordsSocioemotional selectivity theoryTransformative learningPsychologyAction researchAction (physics)EmpathyEmotional intelligenceSample (material)Mathematics educationPedagogySocial psychologyDevelopmental psychology

Abstract

fetched live from OpenAlex

The university is an environment that allows the student to be able to diversify different situations, and there is a need to train sensitive and empathetic professionals for a competent society. The objective of this research was to design a didactic strategy to develop socioemotional competencies in students at a university in Lima. A qualitative methodology of non-experimental, cross-sectional, and descriptive design was used. The sample consisted of 34 students and four teachers. The techniques used included questionnaires, observations and interviews. The results showed 11 emerging categories, seven of which were approximate to the a priori category and four were influential in the research. A validated didactic strategy was presented with two lines of action composed of three stages: classroom emotional diagnosis, management of socioemotional teaching activities, and transformative evaluation; all under a model of Social and Emotional Learning, the Theory of Emotional Intelligence, and the epistemological positions of Neuroscience in Learning.

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)0.001
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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.855
Threshold uncertainty score0.165

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.016
GPT teacher head0.297
Teacher spread0.281 · 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 designTheoretical or conceptual
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
Published2024
Admission routes1
Has abstractyes

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