Intelligent Platform of Ideological and Political Education Resources under Digital Education Environment
Bibliographic record
Abstract
With the development of China's society, the construction and utilization of digital educational resources in the field of basic education in China has many years of practical experience and is gradually developing in depth. In this paper, the text quantization in vector space model was discussed in depth, and the improved Term-Frequency Inverse Document Frequency (TF-IDF) formula was described in detail, which aimed to build an intelligent platform for Ideological and Political Education (IPE) resources. In this paper, 200 students and 50 ideological and political teachers were investigated by questionnaire. It could be seen from the questionnaire data of teachers that only 20.00% of teachers were very satisfied with the application of digital education resources in the existing ideological and political discipline teaching. According to the data of the group experiment of 200 students, before the experiment, the scores of learning efficiency and learning achievement of the experimental group were 5.00 and 5.30 respectively, and the scores of the control group were 5.10 and 5.40 respectively. After the intervention experiment of IPE resource intelligence platform, the learning efficiency and learning achievement scores of the experimental group were 7.90 and 7.60 respectively, and the scores of the control group were 6.20 and 6.10 respectively. It was not difficult to see that the intellectual platform of IPE resources had a promoting effect on students' learning and was worthy of further promotion and application.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".