The integration of knowing and doing in the teaching-learning process of professional courses
Bibliographic record
Abstract
Engineering education focuses on cultivating students' practical and innovative abilities. It not only requires students to master theoretical knowledge but also to apply that knowledge to solve practical problems, achieving a balance between knowing and doing. However, there is currently an issue in engineering education where there is an overemphasis on knowing and a lack of doing, resulting in students having insufficient skills in integrating knowledge, problem-solving and independent thinking. Students may have acquired the knowledge but struggle with the implementation. To enhance students' ability in practical problem-solving, we propose a teaching model that is project-driven and cooperative-group-based. This model incorporates several formative assessment methods, such as inter-group peer evaluation and individual contribution assessment within the group. It was implemented in the course of "Mechanical Design" at an applied research university. The results show that students' learning interest is significantly increased, with more students participating in innovation competitions, and their practical skills are greatly improved. We hope that this "project-driven & cooperative-group-based" teaching model proposed in this paper can provide a useful reference for the teaching of professional courses in engineering education.
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".