A novel instructional design for the course of engineering drawing under the emerging network teaching
Bibliographic record
Abstract
Moral cultivation is an important goal of higher education, and the curriculum of ideological and political education is an important way to carry out ideological and political education. The course of engineering drawing, which has a wide audience, is a professional basic course of engineering and is the precursor to educating students. Affected by COVID-19, college teaching has been switched to online teaching. How to carry out high-quality professional courses and ideological and political education in online teaching is an important problem faced by college teachers. This paper is based on the network teaching platform. It constructs rich teaching resources to realize diversified and efficient teaching. It deeply excavates the new elements of ideological and political education in the epidemic situation. These elements are integrated into the teaching process to realize the collaborative education of knowledge and ideological education. Furthermore, it extracts new cases of ideological and political education outside the classroom. This is in combination with major social needs to guide students to establish lofty aspirations. The goals here include solving major national needs, and realizing all-round ideological and political education throughout the entire process. Finally, the work can improve the teaching quality of professional courses, improve students' ideological and political quality, and achieve the trinity of knowledge transfer, ability training, and value guidance.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.025 | 0.004 |
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".