Research on Innovation and Entrepreneurship Education Based on Experimental Research under Artificial Intelligence Technology
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
With the rapid development of science and technology, in the face of the needs of social development, colleges and universities undoubtedly need to shoulder the important task of talent training and education reform in innovation and entrepreneurship.In this paper, an intelligent learning model is constructed by using artificial intelligence technology.The model takes the subject knowledge graph as the core support, and combines the learning path recommendation algorithm to provide digital and intelligent support for innovation and entrepreneurship education.On this basis, the objectives of innovation and entrepreneurship education are formulated, and the framework of innovation and entrepreneurship education system is established based on the intelligent learning model in this paper, and the cycle model of innovation and entrepreneurship education based on the intelligent learning model is proposed, and the model is experimentally studied.The AUC values and F1 values of the proposed algorithm in the three datasets are higher than 0.85 and 0.80.Compared with the traditional model, the average value of recommendation bias decreased by 8.56, and the evaluation satisfaction increased by 0.126.In the teaching experiment, the overall average score of the innovation and entrepreneurship education model based on this paper was 4.364, which was 1.129 higher than before.Compared with the traditional innovation and entrepreneurship education, it is increased by 0.693, indicating that the innovation and entrepreneurship education model in this paper can promote the all-round development of students' ability level and play a positive guiding role in the development and reform of innovation and entrepreneurship education.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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