Evaluation Methods of Ideological and Political Education in the Context of Modern Distance Education
Bibliographic record
Abstract
Along with the continuous growth of information technology, the deep integration of China’s traditional education model and information technology is gaining more and more attention. Online education has occupied an increasing proportion in the study life of college students and become an indispensable way of learning for them. However, there have been problems with the low use of teaching resources and insufficient content of network resources in the current college social political education class. This article aimed to explore the study of the evaluation methods of ideological and political teaching in the context of modern distance education and to use the analytic hierarchy process (AHP) to help analyze how to better carry out distance education. When evaluating the atmosphere and effect of ideological and political education, 31.16% felt very satisfied with classroom interaction, and 46.35% felt very satisfied with classroom discipline indicators among graduate students. The percentage of those who felt very satisfied with the indicator of student engagement was 31.95%. The percentage of those who felt very satisfied with the indicator of strong academic atmosphere was 31.15%. Therefore, it can be seen that students are optimistic about the ideological and political teaching in the context of distance education.
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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 | 0.034 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".