Exploration on Teaching Reform and Construction of the Curriculum of "Integration of Professional and Innovation" in Automobile CAD
Bibliographic record
Abstract
In the new era of innovation-driven development, it is particularly necessary for college students to carry out integrated education that combines professional theory and practice with knowledge and skills of innovation and entrepreneurship. The Automobile CAD curriculum is one of the important professional curriculums in vehicle engineering. This curriculum is a professional foundation curriculum that combines practical and application nature. Based on the concept of "integration of professional and innovation", a teaching module with strong practicality and different from traditional classroom teaching is set up to strengthen students' practical ability of innovation and entrepreneurship. Relying on various discipline competitions and teacher research projects, innovation and entrepreneurship, practice cultivation of talents and production learning research are integrated, which increases the interest of the curriculum and enhances the learning initiative of students, so as to continuously improve the quality of curriculum teaching. The author mainly discusses the ideas of curriculum construction from the aspects of teaching content, teaching conditions, teaching methods and assessment methods. Through the new teaching mode, it can achieve good teaching effect and provide a reference for cultivating innovative and application-oriented talents in vehicle engineering.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".