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Record W4385520577 · doi:10.5430/ijhe.v12n5p21

Exploration and Practice of Integrating the Shangshan Culture into Art and Design Teaching in China’s Higher Vocational Colleges

2023· article· en· W4385520577 on OpenAlexvenueno aff
Qian Chen

Bibliographic record

VenueInternational Journal of Higher Education · 2023
Typearticle
Languageen
FieldComputer Science
TopicDigital Media and Visual Art
Canadian institutionsnot available
FundersMinistry of Education of the People's Republic of China
KeywordsVocational educationCivilizationChinaSociologyPotteryMathematics educationPedagogyVisual artsArtHistoryPsychologyArchaeology

Abstract

fetched live from OpenAlex

Archaeological authorities consider Shangshan culture, a unique name for China’s outstanding local culture, to be the origin of the world’s painted pottery civilization, Chinese farming villages, and the world’s rice civilization. Shangshan culture is more than 3,000 years old compared to the local civilization discovered by historians. We hypothesized that introducing art and design majors into China’s vocational colleges is an effective method for promoting local culture. Integrating local culture into art and design teaching in China’s vocational colleges provides diversified teaching resources, heightens students’ sense of cultural identity, improves their creative performance, and extends local culture’s influence. Therefore, by analyzing the current status of art and design teaching in China’s vocational colleges, we highlighted local culture’s role in the teaching process. We adopted a quasi-experimental design method to conduct an experiment on 90 students from a vocational college majoring in an art and design course. These students learned about the integration of Shangshan culture into art and design under their teachers’ guidance and applied their learning to their artistic creations. This study’s results will help better integrate local culture into art and design teaching, and consequently, achieve a win-win outcome.

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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.630
Threshold uncertainty score0.226

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 studies0.0000.000
Scholarly communication0.0000.002
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 head0.037
GPT teacher head0.369
Teacher spread0.332 · 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 designTheoretical or conceptual
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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