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Record W2117676845 · doi:10.5539/ass.v10n14p179

Needs Assessment for the Development of Entrepreneurship Curriculum for a Master’s Degree Program

2014· article· en· W2117676845 on OpenAlexvenueno aff
Aree Naipinit, Thongphon Promsaka Na Sakolnakorn, Patarapong Kroeksakul

Bibliographic record

VenueAsian Social Science · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicHigher Education and Employability
Canadian institutionsnot available
FundersKhon Kaen University
KeywordsBachelorEntrepreneurshipBachelor degreeProfessional degreeTeamworkCurriculumDegree programPsychologyGraduate degreeAssociate degreeDegree (music)Medical educationMathematics educationPedagogyManagementPolitical scienceMedicine

Abstract

fetched live from OpenAlex

The objective of this study is to study the opinion of entrepreneurs toward an entrepreneurship degree, to study the opinion of bachelor’s degree students toward a master’s degree in entrepreneurship, and to study the guideline for a master’s degree in an entrepreneurship program. In this study, we used the quantitative method within the questionnaire provided to entrepreneurs and bachelor’s degree students, and we analyzed the results using mean and standard deviation. We also utilized a qualitative method using small group discussion by inviting five academics to discuss the guidelines for a master’s degree in an entrepreneurship program. The results of this study show that the entrepreneurial skills most required are communication and collaboration, the skill of teamwork is higher amongst graduates from master degree programs, and that most bachelor degree students who wish to study in the graduate program think about job opportunities first (in both the public and private sector) and hope that graduate study will increase their knowledge, skills, experience from knowledge and knowledge-sharing in class, and will result in a new way of thinking. In addition, problem-based learning and active learning are very important for a master’s degree program.

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 categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.821
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.081
GPT teacher head0.405
Teacher spread0.324 · 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.

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

Citations3
Published2014
Admission routes1
Has abstractyes

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