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Record W4409018541 · doi:10.5539/hes.v15n2p254

The Development of Instructional Packages using Growth mindset for Enhancing Positive Psychological Capital of Among Higher Education

2025· article· en· W4409018541 on OpenAlexvenueno aff
Jittinun Boonsathirakul, Kumaree Pholpasee, Carlos Boonsupa

Bibliographic record

VenueHigher Education Studies · 2025
Typearticle
Languageen
FieldComputer Science
TopicEducation and Learning Interventions
Canadian institutionsnot available
Fundersnot available
KeywordsMindsetPsychologyHigher educationCapital (architecture)Mathematics educationStatistical analysisApplied psychologyMedical educationEconomic growthComputer scienceEconomicsMedicineStatisticsGeographyMathematics

Abstract

fetched live from OpenAlex

This study aimed to develop instructional packages using a growth mindset framework to enhance positive psychological capital among students of the Faculty of Education, Kasetsart University. The sample consisted of 30students enrolled in the course on Educational Psychology and Guidance for Teachers, selected by purposive sampling. The research employed a positive psychological capital scale and a feedback questionnaire on the activities. Statistical analysis included mean, standard deviation, and the wilcoxon signed-rank test. The results indicated that the instructional packages effectively enhanced positive psychological capital, with the mean score increasing from 3.93 to 4.24, which is at the highest level. The wilcoxon signed-rank test showed that the p-value was less than 0.05, indicating that these iprovements were statistically significant at the .05 level. Additionally, students' feedback on the activities was positive, indicating that the activities helped enhance self-understanding, motivation, and a more positive outlook on themselves and their lives.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.938
Threshold uncertainty score0.339

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.057
GPT teacher head0.406
Teacher spread0.349 · 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
Published2025
Admission routes1
Has abstractyes

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