An empirical study on the design of intelligent auxiliary teaching system and teaching interaction effect of Ideological and Political classes in universities based on fuzzy comprehensive evaluation method
Bibliographic record
Abstract
Network teaching has become an important way of teaching reform in current higher education and has been applied in the education of various courses.This paper proposes a kind of intelligent auxiliary teaching system based on P2P mode, and researches the realization of the system with the example of Civics course.The construction of "Civics Course Teaching Evaluation System" is systematically discussed by using the fuzzy comprehensive evaluation method, and the weights of the indicators are calculated by entropy weighting method and hierarchical analysis method.Taking the teaching of Civics and Political Science in a university in Guangdong Province as the research object, the intelligent teaching system proposed in this paper is applied to evaluate the interactive effect of teaching with the evaluation system constructed in this paper.The evaluation analysis shows that the school's evaluation results of all indicators are above 80 points, and the overall teaching rating of its Civics and Political Science course is 86.33, in which social merit, teaching equipment, teaching expression, and professional ethics have the highest scores of 94.37, 92.32, 89.02, and 88.52, respectively.It shows that the intelligent auxiliary teaching system for Civics proposed in the article is well applied in actual teaching.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".