The Market-Oriented Education Approach Brings Challenges to the Practice: Based learning Approach in Pre-school Higher Education
Bibliographic record
Abstract
Universities in Chinaare encouraged by the Chinese government to adopt a practice-based learning approach in the Higher Education sector. Such emphasis exerts great impacts on the traditional practice-basedpre-schoolHigher Education and has alsoimposed some challenges ontoacademics in Universities. The market-oriented approach requires Universities to emphasize employability skills that meet the demands of delivering a modernized and international pre-schooleducation. Universities need to adapt their curriculum design to embed practical training on employability skills into the curriculum.Building on our existing knowledge, the pre-school curriculum is aiming to work with pre-school practitioners. Bridging the gap between University teachingandpractical skills required by the pre-school sector, this process facilitates practice-based learning for students and improves research for academics. Piloting practice-based learning allows Universities to focus on enhancing employability skills and helping Universities adapt their curriculum to achieve the desirable employability skills they want their graduates to have. As a result, the practice-based learning approach informs Universities’ curriculum design and quality standards so that graduates develop employable attributes.
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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 | 0.016 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.013 |
| Scholarly communication | 0.012 | 0.007 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".