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Record W3117349630 · doi:10.5430/wje.v10n6p143

Management Strategies for Enhancing Students’ Entrepreneurship in Industrial and Community Education Colleges of the Northeastern Thailand

2020· article· en· W3117349630 on OpenAlexvenueno aff
Pha Agsornsua, Prayuth Chusorn, Vanit Prasertporn, Adul Pimtong, Natthasak Samranruen

Bibliographic record

VenueWorld Journal of Education · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicVocational and Entrepreneurial Education
Canadian institutionsnot available
Fundersnot available
KeywordsEntrepreneurshipDescriptive statisticsVisionData collectionEntrepreneurship educationDescriptive researchSample (material)PsychologyMedical educationResearch designMarketingSociologyBusinessSocial scienceStatisticsMathematicsMedicine

Abstract

fetched live from OpenAlex

The objectives of this research were to study the needs assessment and to develop management strategies for enhancing the entrepreneurship of students. This study was conducted by utilizing Mixed Methods Research. The sample group used in this study consisted of School Directors and Teachers totaling 226 persons under the Northeastern Industrial and Community Education Colleges in Thailand. The tools used for data collection were questionnaires and strategic evaluation forms. Data were analyzed through descriptive statistics, namely, frequency distributions, percentages, means, standard deviations, and PNI Modified.The results of the research revealed the followings: 1)In all, the current conditions of management for enhancing the entrepreneurship of the students were at a “High” level. Regarding the desired conditions, it was, in general, at the “Highest” level. As for the needs priority, it was found that the most needed aspects were the characteristics of Entrepreneurship, the management of an ‘Entrepreneurial Incubation Center, and Teacher Development, respectively. 2) The management strategies for enhancing the entrepreneurship of students comprised visions, missions, targets, of 9 strategies with 62 measures, and 67 indicators. Some recommendations and suggestions for future research were introduced.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.272
Threshold uncertainty score0.391

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 head0.060
GPT teacher head0.343
Teacher spread0.283 · 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
Published2020
Admission routes1
Has abstractyes

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