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Record W4411154205 · doi:10.5539/jel.v14n5p324

Needs Assessment of Participatory School Management for Educational Quality Development of Small Primary Schools Under the Office of Basic Education Commission in the Northeastern Region

2025· article· en· W4411154205 on OpenAlexvenueno aff
Nattanan Vannasuk, Suwat Julsuwan, Pacharawit Chansirisira

Bibliographic record

VenueJournal of Education and Learning · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicSchool Leadership and Teacher Performance
Canadian institutionsnot available
Fundersnot available
KeywordsCommissionPedagogyQuality (philosophy)Primary educationMathematics educationPsychologySociologyPolitical scienceMedical education

Abstract

fetched live from OpenAlex

This research aimed to study the current conditions, desirable conditions, and needs assessment in participatory school administration of small primary schools under the Office of Basic Education Commission in the Northeastern region. The sample consisted of 367 teachers from small primary schools under the Office of Basic Education Commission in the academic year 2024. The research instrument was a 5-rating scale questionnaire assessing current and desirable conditions. The statistics used for data analysis included frequency, percentage, arithmetic mean, standard deviation, and Priority Needs Index (PNImodified). The findings revealed that the current condition was at a high level (x̄ = 3.92), and the desirable condition was at the highest level (x̄ = 4.54). The overall needs assessment (PNImodified = 0.16) indicated that academic administration had the highest priority needs (PNImodified = 0.26), followed by personnel administration (PNImodified = 0.15), general administration (PNImodified = 0.14), and budget administration (PNImodified = 0.10), respectively. The research results suggested that emphasis should be placed on building cooperation among all stakeholders for continuous and sustainable educational quality development.

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)0.004
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 score0.171
Threshold uncertainty score0.658

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
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 head0.124
GPT teacher head0.420
Teacher spread0.297 · 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
Published2025
Admission routes1
Has abstractyes

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