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Record W4385343625 · doi:10.5430/ijhe.v12n4p55

Development of the Chinese Pop Music Module in Enhancing Chinese Culture Cognition, Learning Motivation, and Cross-cultural Adaptability of International Students in Chinese Higher Vocational Colleges

2023· article· en· W4385343625 on OpenAlexvenueno aff
Qiang Wang, Salmiza Saleh

Bibliographic record

VenueInternational Journal of Higher Education · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicIdeological and Political Education
Canadian institutionsnot available
Fundersnot available
KeywordsVocational educationAdaptabilityChinaPsychologyChinese cultureCognitionDanceMathematics educationPedagogyPolitical scienceVisual artsManagementArt

Abstract

fetched live from OpenAlex

Under the Belt and Road initiative, the number of international students in China has grown significantly. How to help international students improve their low level of Chinese culture cognition, learning motivation, and cross-cultural adaptability in China has become a hot topic. This study developed the current Chinese pop music module to solve the main problems faced by international students in the first academic year of Chinese higher vocational colleges. The ASSURE model was used to develop the teaching process of the current Chinese pop music module to achieve the teaching objective and improve teaching effectiveness. Teaching content of the developed module included Chinese campus music, Chinese minority music and dance, Chinese rock music, Chinese rap, Chinese-style songs, and Chinese R&B. The Chinese pop music module with improved teaching content was then discussed in detail.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

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 communication0.0000.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.023
GPT teacher head0.399
Teacher spread0.376 · 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 source (direct Gemma or distilled Codex), 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
Published2023
Admission routes1
Has abstractyes

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