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Record W4407378893 · doi:10.5430/jct.v14n1p134

Factors Affecting the Formation of Career Orientation Capacity for Secondary School Students Through Organizing Experiential Activities

2025· article· en· W4407378893 on OpenAlexvenueno aff
Duyen Thi Le, Dieu Thi Thanh Bui

Bibliographic record

VenueJournal of Curriculum and Teaching · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicCareer Development and Diversity
Canadian institutionsnot available
Fundersnot available
KeywordsExperiential learningPsychologyOrientation (vector space)PedagogyMathematics educationMathematics

Abstract

fetched live from OpenAlex

Background: Career guidance programs in secondary education are crucial for directing students’ career paths, particularly through experiential learning and related activities. Teacher competence, institutional support, student motivation, and social influence are significant factors affecting the effectiveness of these programs. Purpose: This study aims to identify and analyze the factors influencing the organization of career guidance activities for high school students in Da Nang City, Vietnam, with an emphasis on those that enhance students' career orientation capabilities. Methods: A quantitative survey involving 223 high school teachers across various districts in Da Nang City, Vietnam was conducted. The analysis was conducted with SPSS, employing descriptive statistics to evaluate the influence of various factors. Results: The results indicated that the abilities of educators in the design and implementation of career guidance activities produced the greatest influence, followed by experiential activities, school resources, and organizational support. Student motivation and social influences, including familial and societal expectations, exhibited moderate effects. Conclusion: The research underscores the necessity for adequately funded and supported career guidance programs within educational institutions to effectively improve career guidance for students. Future research may enhance these findings by examining various educational contexts and integrating qualitative data.

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: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.468
Threshold uncertainty score0.698

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 studies0.0010.000
Scholarly communication0.0000.001
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 head0.033
GPT teacher head0.319
Teacher spread0.286 · 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 designQualitative
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

Citations2
Published2025
Admission routes1
Has abstractyes

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