Construction of Ideological and Political Education in Universities Based on Intelligent Digital Education
Bibliographic record
Abstract
With the development of the times, there is an increasing emphasis on Ideological and Political Education (IPE) for students. Good IPE can improve the quality of students, and enable them to become better citizens in the society. This paper studied the construction of IPE in universities based on intelligent digital education, which aimed to study whether intelligent digital education could improve the quality of IPE. This experiment selected a school that used intelligent digital education and a school that used traditional education. By comparing the results of the five classes of Mao Zedong Thought and the theoretical system of socialism with Chinese characteristics, the principles of Marxist philosophy, the situation and policy, ideological and moral cultivation and legal basis in the two schools, the role of intelligent digital education was highlighted. The scores of Mao Zedong Thought and the theoretical system of socialism with Chinese characteristics in the five classes using intelligent digital education were 86, 90, 92, 87, and 85 respectively. The scores of the five classes using intelligent digital education on the principles of Marxist philosophy were 79, 75, 73, 71, and 78 respectively. The situation and policy scores of the five classes using intelligent digital education were 70, 74, 79, 82, and 77. The scores of ideological and moral cultivation and legal basis of the five classes using intelligent digital education were 85, 90, 93, 88, and 92 respectively. Compared with the traditional IPE, these data had a good improvement in the test scores of students, which proved that intelligent digital education was really helpful for IPE in universities.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 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".