Evaluation System of College Ideological and Political Education Index Based on Data Mining Algorithm
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
The Intellectual and Political (IAP) education provided in colleges and universities is a significant component of higher education and is crucial to developing the socialist cause with Chinese characteristics and enhancing the IAP abilities of college students. The construction of college IAP education index assessment system based on multimodal learning type data mining algorithm can solve the current situation of the lack of college IAP education index assessment system. Therefore, this paper constructed an assessment system of college IAP education indicators based on data mining algorithm. It was a system that conformed to the requirements of the development of the times and the laws of education and had good human-computer interaction. Through the experiment, the comprehensive satisfaction score of the sample to the assessment system of college IAP teaching indicators based on data mining algorithm was about 4.19, and the comprehensive satisfaction score to the traditional assessment system of college IAP teaching indicators was about 2.87. The college IAP education index assessment system based on data mining algorithm was superior to the traditional college IAP education index assessment system, which made the assessment activities effectively play the role of summing up experience, learning lessons, promoting work improvement, establishing goal orientation, etc.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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