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Record W4308826696 · doi:10.5430/jct.v11n8p281

Analysis of the Relationship among Career Education Program Participation and Satisfaction, Work Value, Career Goal Setting in Higher Education

2022· article· en· W4308826696 on OpenAlexvenueno aff
Joo-Young Jung

Bibliographic record

VenueJournal of Curriculum and Teaching · 2022
Typearticle
Languageen
FieldComputer Science
TopicEducation and Learning Interventions
Canadian institutionsnot available
FundersKosin University
KeywordsPsychologyWork (physics)Medical educationStructural equation modelingCareer developmentValue (mathematics)Work motivationHigher educationCareer educationCareer portfolioCognitive Information ProcessingJob satisfactionApplied psychologyPedagogySocial psychologyVocational educationMedicinePolitical scienceMathematics

Abstract

fetched live from OpenAlex

This study aimed to analyze the causal model of career education program participation, satisfaction, and career goal setting mediated by work values in higher education. Also, this study analyzed the differences between departments. In this study, data from the 2018 Graduate Occupational Mobility Survey (GOMS), the most recent data, was used. The Graduates Occupational Mobility Survey is the largest short-term panel survey of a representative sample of Korean Graduates. The GOMS is conducted annually and has its results compiled each year. Structural equation modeling and multiple SEM were used for this study. The results were as follows. First, career education program participation had a statistically significant positive effect on extrinsic and intrinsic work values and career goal setting. Second, career education program satisfaction had a statistically significant positive effect on intrinsic work values and career goal setting, except for extrinsic work value. In addition, only the intrinsic work values positively affected career goal setting. Third, the career education program satisfaction impacted intrinsic work values, and the intrinsic work value impact on career goal setting was the difference between department and majors. In conclusion, this study proposed the provision of career education programs considering the difference between departments and majors in higher education.

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 score0.046
Threshold uncertainty score0.269

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.029
GPT teacher head0.324
Teacher spread0.295 · 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
Published2022
Admission routes1
Has abstractyes

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