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Record W4406403365 · doi:10.5539/jel.v14n3p76

Development of an Active Play Learning Model to Promote Executive Functions for Elementary School Students

2025· article· en· W4406403365 on OpenAlexvenueno aff
Somkiat Kongthanajindasiri, Manop Sungkaew, Benchaporn Sawangsri

Bibliographic record

VenueJournal of Education and Learning · 2025
Typearticle
Languageen
FieldPsychology
TopicChild Therapy and Development
Canadian institutionsnot available
Fundersnot available
KeywordsPsychologyMathematics educationActive learning (machine learning)PedagogyComputer science

Abstract

fetched live from OpenAlex

In this study, we aimed to develop an active play learning model to enhance cognitive thinking skills in elementary school students. We employed a qualitative research methodology, targeting health and physical education teachers from schools in Suphan Buri Province in Thailand. We collected data through individual surveys and in-depth interviews and performed data synthesis using documents and focus groups to guide the model’s development. The research findings revealed that teachers have a moderate understanding of cognitive thinking skills and the active play learning model. Although sports activities are regularly incorporated into lessons, they are not done so effectively enough to develop cognitive thinking skills. However, teachers recognize the importance of organizing activities to help students develop social skills through play. Therefore, we recommended additional training to enhance the active play learning model’s knowledge and understanding and ensure its effective implementation in teaching and learning. Based on data synthesis, we focused on three key areas: working memory skills, inhibitory control and reflective thinking skills, and cognitive flexibility skills. We utilized a variety of games and activities, such as rock-paper-scissors, imaginative block games, and treasure hunts using maps, to stimulate skill development in each area.

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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.758
Threshold uncertainty score0.329

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 head0.024
GPT teacher head0.389
Teacher spread0.365 · 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 designOther design
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
Published2025
Admission routes1
Has abstractyes

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