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Record W4400017255 · doi:10.5539/elt.v17n7p82

Motivation Differences Between Different Groups of International Business Students in Guangdong Communication Polytechnic

2024· article· en· W4400017255 on OpenAlexvenueno aff
Wei-Wei Tu, Mohamad Jafre Bin Zainol Abidin, Fengfeng Zhang, Weizhi Chen

Bibliographic record

VenueEnglish Language Teaching · 2024
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicManagement and Marketing Education
Canadian institutionsnot available
Fundersnot available
KeywordsPsychologyMathematics educationPedagogy

Abstract

fetched live from OpenAlex

This study investigated the motivations of 332 students from Guangdong Communication Polytechnic, conceptualizing the students’ motivations for studying international business. The students answered a motivation questionnaire with 30 Likert-type questions. This questionnaire was built to collect information about students’ motivations to study international business for international business teachers and researchers. The questionnaire was adopted from Glynn (2009). The grading of the questionnaire was reliable and related to the students’ preparation before entering the polytechnic, their grade point averages in international business courses, and their faith in the relevance of international business courses for their future occupations. The conceptualization of motivation for studying international business was divided into five aspects, including internal motivation and individual relevance, self-efficiency and judgement anxiety, independence, occupation motivation, and degree motivation (Glynn, 2009). The result of this study showed that in their final year, international business students achieved the best performance in internal motivation and individual relevance, self-efficiency, and independence for international business learning. Second-year students had the best extrinsic motivation. The freshmen reported the most anxiety in assessments.

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)0.001
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 score0.027
Threshold uncertainty score0.537

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.014
GPT teacher head0.269
Teacher spread0.255 · 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".

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Citations0
Published2024
Admission routes1
Has abstractyes

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