A Study on the Application of Video Teaching Method in College Tennis Teaching in the Background of Mobile Internet
Bibliographic record
Abstract
This study implements and explores the relevance and efficacy of video teaching methods within the context of teaching tennis to college students. By comparing the learning outcomes of traditional classes and video-aided classes through experiments, this study highlights the innovative and practical value of video teaching methods for university-level tennis instruction in China. Various issues encountered during the experimental operation are statistically analyzed and addressed, with our insights and suggestions on specific issues also discussed. The video teaching method not only compensates for the limited opportunity for independent practice during traditional physical education classes but also offers a novel approach to enhance education and teaching. Through literature review, mathematical statistics, and experimental methods, this study conducts an experimental investigation on the tennis techniques of students enrolled in the 2019 tennis class at Guangdong Industrial and Commercial Vocational Technology University. The findings reveal that while video teaching methods do not significantly impact the forehand stroke performance of the students tested, they considerably affect the technical evaluation outcomes. The students in the video-aided class substantially outperformed those in the traditional class regarding the technical evaluation results of forehand strokes. Despite the video teaching method having a marginal effect on students' backhand stroke test scores, it significantly influenced the technical evaluation results. Accordingly, students in the video-aided class had significantly better backhand stroke technical evaluation results compared to those in the traditional class.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".