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Record W4392190221 · doi:10.55908/sdgs.v12i2.3398

SUFFICIENCY ECONOMY LEARNING CENTERS IN SATUN PROVINCE’S ISLAND SCHOOLS: A STRATEGIC APPROACH TO CURRICULUM MANAGEMENT FOR CAREER COMPETENCY

2024· article· en· W4392190221 on OpenAlexaff
Rungchatchadaporn Vehachart, Phatsarabet Wetviriyasakul, Venus Srisakda, Orapin Tipdech, Somkiat Yangchin

Bibliographic record

VenueJournal of Law and Sustainable Development · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicHigher Education and Employability
Canadian institutionsImpact
FundersInstitute for the Promotion of Teaching Science and Technology
KeywordsCurriculumPedagogyBusinessPolitical sciencePsychology

Abstract

fetched live from OpenAlex

Purpose: The aim of this study is to investigate the implementation of Sufficiency Economy Learning Centers (SELCs) in schools located on islands within Satun Province. Specifically, the study aims to explore how these SELCs strategically contribute to curriculum management, with a focus on enhancing career competency among students. Through this research, the aim is to analyze the effectiveness of integrating sufficiency economy principles into the educational framework of island schools, thereby facilitating students' acquisition of skills and knowledge essential for their future careers. Theoretical framework: The theoretical framework for this study draws upon several key concepts and theories to provide a comprehensive understanding of the implementation of Sufficiency Economy Learning Centers (SELCs) in island schools and their impact on curriculum management for career competency. Design/Methodology/Approach: The target groups are vocal speakers and 8 people from the Thaksin University research team; 4 people from the Primary Educational Service Area Office in Satun; 10 experts in language, science, math, and technology; 19 teachers in Ban Ko Lipe School; 30 people from community leader, village sage and interested people participating in the pilot project of learning center development; and 127 people from the evaluation of using the project. Findings: The research findings found that one integrated SMT competency-based curriculum of Ban Ko Lipe school and online platform of SMT competency-based learning center of Ban Ko Lipe school displayed Ecotourism such as snorkeling, features of landscape, climate, high/low tide, waxing/waning moon 2) Fishing such as fishing gear, methods of catching each type aquatic animals 3) Local products such as long tail boats produced from seashore screw pines. The platform development of SMT competency-based learning of students in the Lipe island area created the administrative collaboration of the community using SMT competency-based curriculum followed the sufficiency economy philosophy of Lipe island area for the career path of the youth to have ways of life through the sufficiency economy philosophy Satun Province. The evaluation of 127 curriculum users found that the SMT level was at the highest in every aspect 1) administration 2) premises 3) learning resources 4) network. Research, Practical & Social implications: This study examines the implementation of Sufficiency Economy Learning Centers (SELCs) in island schools in Satun Province, focusing on their impact on students' career readiness. The research uses a mixed-methods approach, combining qualitative and quantitative techniques. The findings can inform policy recommendations, guide professional development, and prompt curriculum revisions. The study also emphasizes community engagement, empowerment, and sustainable development, highlighting the importance of SELCs in promoting socio-economic resilience. Originality/Value: This study explores the implementation of Sufficiency Economy Learning Centers (SELCs) in island schools in Satun Province, focusing on their impact on career competency development. It provides context-specific insights, practical relevance, and theoretical advancements in education, sustainable development, and career development, bridging the gap between theory and practice.

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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.939
Threshold uncertainty score0.341

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 head0.021
GPT teacher head0.288
Teacher spread0.267 · 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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