A general method of analyzing the correlation between sustainability and curriculum of higher education in China
Bibliographic record
Abstract
With the increasing awareness of sustainable development in higher education institutions, it has become an essential part for setting training program and curriculum. The key step is how to integrate sustainable development requirements into professional courses among college students education to make the theoretical groundwork possible in all disciplines. The purpose of this paper is to present a systematic approach through top-bottom to integrate sustainability contents into curriculum as well as quantifying influence degree of different courses on sustainable development. It mainly takes the training program setting of higher education institutions as the research object, and analyzes the relevance of training goals, graduation requirements, core courses, curriculum system and sustainability factors. Taking general education courses, subject basic courses, professional courses and optional courses as examples, the relevance between sustainable factors and curriculum design is quantified combining qualitative and quantitative analysis method for providing valuable reference for decision makers. All majors of University of Shanghai for Science and Technology (USST) are analyzed through considering whether or not containing sustainability factors like environment, society and economy. The data are collected from the training program of all majors in USST. Furthermore, the major of mechanical design, manufacturing and automation in USST is used as a case study to reveal the importance of integrated sustainable factors, and the significance of higher education of engineering specialty for the implementation of sustainable development.
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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 | 0.008 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.009 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".