Construction of an Interactive Network for Teaching Physical Education in a Digital Web Analytics Model
Bibliographic record
Abstract
In educational research, more and more scholars recognize the importance of teaching interaction network for learning, and they find that "interaction" is not only the method of learning, but also the learning process itself.Social network analysis provides a new way to study teaching interaction.Through the study of social network analysis, this paper proposes the construction method of teaching interaction network for physical education.In this paper, we take four real physical education courses in L school as the research object to conduct in-depth research, obtain the physical education classroom teaching interaction behavior data, and construct the teaching interaction network.The results of the study show that in the interaction network of the four physical education teaching courses, the teaching behaviors of the community network of physical education classroom 1 are significantly concentrated in B3, B4, B5, and B6, course 2 is concentrated in B4, B5, B6, B9, and B10, the teaching interactive behaviors of physical education classroom 3 are significantly concentrated in B2~B6, and the significant physical education teaching interactive behaviors of course 4 are concentrated in B2, B4, B5, and B6.From the degree-centeredness analysis, there are 33 marginal learners with the number of stored interactions less than or equal to 2 in physical education teaching interactions, which indicates that in this paper's study of physical education teaching interactions, teachers do not pay enough attention to teaching interactions in a comprehensive way.By summarizing the theoretical basis and practical significance of teaching interaction and social network analysis, it proves that the network construction of teaching interaction in this paper is effective, and at the same time, it also provides a new idea for physical education teaching courses.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".