Evaluation Methods of Ideological and Political Education in the Context of Modern Distance Education
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it