A Study on the Necessity of Introducing Teaching-plan-telling into Physical Education Undergraduates’ Courses in Normal Universities
Bibliographic record
Abstract
The cultivation target of physical education major in normal universities is mainly physical teachers’ qualification in basic education. Training of teaching-plan-telling on students of sports teaching major in normal universities has significant meaning to enhance the quality of students in a comprehensive way, realize the target of professional education and facilitate students of sports in normal universities to adapt to basic education as soon as possible after graduation.Through analysis in the essence of teaching-plan-telling and disadvantages that exist at present in physical internship among students who major in physical education teaching in normal universities, this article explained the role of introducing teaching-plan-telling in teaching of students of sports education in normal universities according to the requirement of research on classroom teaching under the background of new course. The research results indicated that teaching-plan-telling is a bridge for students of sports in normal universities to go towards practice, and can encourage them to learn carefully the educational theories, improve the quality of educational internship and shorten the time from a student of sports in a normal university to a qualified sports teacher. Teaching-plan-telling is an effective form to enhance the teaching capacity and teaching and research capacity of students of physical education in normal universities, so it ought to be regarded as an important content in professional teaching in physical education of normal universities.
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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 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".