Application of a multi-course linked project-based teaching approach in a mechanical course
Bibliographic record
Abstract
Project-based teaching aims to enhance students' understanding of knowledge through the practice of projects. In recent years, project-based teaching has been more and more widely used in college teaching. Taking students of mechanical majors as an example, students need to learn several professional courses in mechanical design, such as "Mechanical Principles", "Mechanical Innovation Design", "Mechanical Design" and "Course Design of Mechanical Design". There is continuity in the knowledge of these courses. In this work, an approach of implementing a project-based teaching approach that links multiple courses within the field of mechanical engineering was explored. The multi-course linked project-based teaching approach allows students to build on their knowledge progressively, reduce learning pressure, enhance learning curiosity and practical skills, and develop their abilities of teamwork, practical, and critical thinking, as well as their project planning and execution skills. The multi-course linked project-based teaching approach introduced in this paper can provide a useful reference for the reform of mechanical professional courses.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".