Perspectives on Art Education for Sustainable Development in Higher Education Institutions in Sichuan Province
Bibliographic record
Abstract
This study examines the current state of Art Education for Sustainable Development (AESD) in higher education institutions across Sichuan Province, focusing on six key dimensions: Goal of Art Education Training (GAET), Art Education System and Mechanism (AESM), Art Education Curriculum System (AECS), Art Practice System (APS), Art Education Faculty (AEF), and Art Education Quality (AEQ). A mixed-methods approach was adopted, combining questionnaires and structured interviews with 384 students, 291 teachers, 274 administrators, and 25 experts. Quantitative analysis, employing descriptive statistics, ANOVA, and Pearson correlation, indicated moderate overall perceptions of AESD. Strengths included alignment with individual development needs and the effectiveness of independent practice systems, while challenges encompassed unclear training objectives, underdeveloped institutional mechanisms, and limited international opportunities. Qualitative findings reinforced these observations, highlighting outdated curricula, insufficient faculty development, and inadequate resource allocation as persistent obstacles. Furthermore, the study identified significant correlations among key variables, with AESM and APS emerging as critical factors in enhancing education quality. Based on these insights, the study recommends redefining training objectives, fostering curriculum innovation, expanding interdisciplinary and international opportunities, and strengthening institutional mechanisms to more effectively align art education with sustainable development goals (SDGs). These findings offer actionable insights to address systemic gaps and leverage institutional strengths, ultimately equipping higher education institutions to prepare stakeholders for meaningful contributions to societal progress and sustainability.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".