Data Mining of Teaching Evaluation of College Physical Education Courses Based on Artificial Intelligence and Multimedia Technology
Bibliographic record
Abstract
Artificial intelligence (AI) and multimedia technology (MT) provide a new platform for college physical education (PE), which plays a positive role in promoting college PE.Combined with the actual situation, some discussions are made on the application of multimedia teaching technology in college PE teaching, in order to better serve the MT teaching of college PE teaching.The popularization and wide application of multimedia teaching technology in education and teaching have caused a series of changes in teaching concepts, teaching design, teaching methods, creative teaching, etc., preparing for the development of teaching.Starting from the teaching quality evaluation methods, the existing problems in the evaluation process were analyzed.These problems are reflected in the retrospective evaluation method, which is not scientific enough to summarize the evaluation results, and it is difficult to track and improve the teaching ideas.Teaching evaluation is a complex system that includes classroom teaching, sports facilities, sports activities, classroom teaching, physical health, supervision and management and many other aspects.Modern educational philosophy generally holds that the classroom teaching process should include formulating clear teaching objectives, selecting the most appropriate teaching methods and using scientific evaluation methods to collect information about correct answers.According to the construction of a comprehensive evaluation system of intelligent algorithm and AI technology, the quality of teaching evaluation has been improved by 21.4% after calculation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".