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Record W4367300621 · doi:10.5539/jel.v12n3p93

The Development of Emotional Quotient Evaluation for Thai University Students

2023· article· en· W4367300621 on OpenAlexvenueno aff
Nattapon Yotha, Arthitaya Khaopraay, Nateethorn Narkprom, Wasinee Wasinee Rungruang

Bibliographic record

VenueJournal of Education and Learning · 2023
Typearticle
Languageen
FieldPsychology
TopicEmotional Intelligence and Performance
Canadian institutionsnot available
Fundersnot available
KeywordsPsychologyGoodness of fitConfirmatory factor analysisEmotional intelligenceStructural equation modelingConstruct validityValidityTest validityReliability (semiconductor)Stratified samplingSocial psychologyStatisticsPsychometricsMathematicsDevelopmental psychology

Abstract

fetched live from OpenAlex

In this study, we designed an evaluation form to assess the emotional quotient of Thai university students. The indicators and components of the evaluation were developed from theories and principles regarding emotional intelligence. After the process of content validity assessment, the evaluation consists of 80 indicators in 5 components of Self-awareness, Self-regulation, Self-Motivation, Recognizing Emotions in Others, and Social Skills. In detail, the evaluation was found to have an index of congruence (IOC) of 0.50-1.00, discrimination of 0.472-0.817, and reliability of 0.991. It was later tested for construct validity with 200 Thai university students selected by stratified sampling method. The method of confirmatory factor analysis was employed. The results of the study indicate the evaluation construct validity as the goodness of fit indices passed the criteria and the evaluation model fits the empirical data. Chi – Square = 87.436, df = 73, Chi-Square /df = 1.197, p-value = 0.119, CFI = 0.997, TLI = 0.995, RMSEA = 0.031, and SRMR = 0.021.

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)0.002
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.660
Threshold uncertainty score0.180

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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 head0.095
GPT teacher head0.439
Teacher spread0.344 · 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

Citations1
Published2023
Admission routes1
Has abstractyes

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