Exploration of Learning-Centred Teaching Mode for "Structural Design for Mechanical Equipment"
Bibliographic record
Abstract
Aiming to address the issue of the failure to comprehend complex course content due to insufficient time for thinking in class and the conflict between the timeliness of teaching content and the development of students' innovative skills, a learning-centred teaching mode is implemented in the education reform of the professional core course "Structural Design for Mechanical Equipment". Two teaching platforms, "Chaoxing" and "Yiwangchangxue", with multi-terminal access are introduced in the online-offline mixed teaching process. Based on these teaching platforms, an initiative-inspired teaching mode of Presentation-Assimilation-Discussion class could be implemented. Activities such as preview, discussion, classroom testing, etc. are assigned to students at different stages of the course, and all the activities are linked together with the presentation made by the students themselves. In addition, through the outcome-oriented achievement evaluation system, teachers can comprehensively and fairly evaluate students' learning progress using activity data from the platforms. This data helps teachers make timely adjustments, tailor teaching to individual aptitudes, and guide students in understanding course content correctly. It also fosters strong innovation skills.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 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".