Research on Curriculum Development and Management Mechanisms for Qingshen Bamboo Weaving in the Context of Industry-Education Integration
Bibliographic record
Abstract
This study applies industrial value chain and educational management theories as a value chain–oriented theoretical lens to systematically examine the determinants of curriculum design and development in traditional craft vocational education, emphasizing optimization strategies within industry–education integration frameworks.Using a mixed-methods research, we conducted questionnaire surveys with 380 stakeholders (350 valid responses) and employed multiple linear regression to evaluate how educational management components influence instructional effectiveness in the case of the Qingshen bamboo weaving curriculum.Empirical findings demonstrate that market demand alignment (β=0.356, p<0.001) exerts the greatest influence on curriculum development and teaching effectiveness, followed by technological empowerment (β=0.349, p<0.001), experiential curriculum design (β=0.291, p<0.001), and inheritors' collaborative teaching ability (β=0.286, p<0.001), with the overall model explaining 47.2% of variance. The study underscores that integrating value chain perspectives into curriculum development effectively connects the educational continuum from “skill transmission—competency development—professional requirements” while promoting systematic optimization of curriculum governance mechanisms and enhancing talent development quality in traditional craft vocational education. Based on empirical analysis, we propose a demand-driven “four-dimensional collaborative curriculum development model” that achieves curriculum value enhancement while optimizing educational resources and multi-stakeholder collaborative governance, providing theoretical foundations and practical guidance for curriculum reform, pedagogical innovation, and educational management modernization in traditional craft vocational education.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".