Exploration on the Development of Physical Education Teaching Integration Mode in Colleges and Universities Based on Artificial Intelligence
Bibliographic record
Abstract
Arti icial intelligence plays an increasingly important role in contemporary education, and it provides new possibilities for the innovation of physical education teaching mode.This paper constructs a college sports teaching integration model based on arti icial intelligence from ive aspects: educators, learners, teaching methods, educational resources and teaching feedback and evaluation.It focuses on designing a precise teaching model PLRSM based on personalized learning resource recommendation by combining learner portrait and learning resource portrait, and takes the recommendation of physical education teaching resources for physical education students as a case study to verify the effectiveness of the proposed algorithm.The results show that compared with the traditional baseline algorithm, the PLRSM algorithm still maintains a better recommendation performance when the data set co-occurrence matrix is extremely sparse, and its correct rate of physical education teaching resources recommendation is 0.80.In addition, compared with the traditional teaching model, the AIbased college physical education teaching fusion model can signi icantly improve the learners' knowledge of physical education subject and course teaching, and its post-test score is higher than the pre-test score 11.525 to 15.436 points.The study provides theoretical support and practical guidance for the application of arti icial intelligence in physical education teaching, and provides a useful reference for promoting the innovation of physical education teaching mode.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".