Teaching Design and Practice of "Competition Teaching Integration with Competition Promoting Teaching"—Take the Teaching of Website Design and Production as an Example
Bibliographic record
Abstract
In order to promote the growth of double-qualified teachers, promote the construction of high-level and structured teaching innovation and unity, and constantly improve teachers' ability to practice teacher ethics, professional teaching ability, comprehensive education ability and self-development ability, Shanxi Shuozhou Shentou Vocational High School set up a professional team to participate in the 2022 National Vocational College Skills Competition teaching ability competition.The participating projects will take the teaching of website design and production project as an example, reconstruct the catalog based on real enterprise projects, develop school-based loose page textbooks, and apply multi-media teaching means such as digital teaching resource library, classroom management and control platform, intelligent teaching assistant for cloud class, e-book, micro-class and micro-video. Therefore, we have transformed the traditional teacher-centred classroom model into a student-centred flipped classroom model, implemented the ideological and political requirements of the curriculum, explored the comprehensive education of the "After-school Competition Certificate", realised the fusion of competition and teaching, and promoted competitive teaching, competitive learning and competitive reforms, so as to provide a certain degree of reference for assisting schools in building high-level schools, promoting pedagogical reforms and improving the quality of teaching.
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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 | 0.008 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".