Chinese-style Apprenticeship System and Digital Teaching Resources: The Inheritance Path of Traditional Crafts
Bibliographic record
Abstract
Chinese traditional crafts represent an important cultural heritage. However, with the advancement of modernization, the inheritance of these crafts has encountered challenges such as a talent gap and the loss of craft carriers. Therefore, this study explores an innovative model integrating the Chinese-style apprenticeship system with digital teaching resources, with the aim of promoting the effective inheritance and innovative development of traditional crafts through vocational education. The study employed case analysis, in-depth interviews, and other research methods to thoroughly examine the implementation pathways and development directions of traditional craft inheritance. The research revealed that innovations within the Chinese-style apprenticeship system, such as the establishment of coherent educational systems, dual identity recognition, curriculum framework development, and teaching content enhancement, significantly improved students' comprehension of traditional craft theories and their mastery of vocational skills. Furthermore, with the support of digital teaching resources, students were immersed in interactive learning environments, which fostered deep and active learning experiences, enabling both the critical inheritance and derivative creation of traditional crafts. The conclusion drawn from this study suggests that the integration of the Chinese-style apprenticeship system with digital teaching resources possesses broad applicability and offers valuable insights for the teaching reform of modern vocational education. Future research may further explore the applicability of this model in other cultural heritage inheritance domains and investigate how generative AI technology can be utilized to facilitate the timely inheritance of traditional crafts.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".