Design and Implementation of Interdisciplinary Knowledge Integration and Teaching Innovation System for Dual Colleges and Universities Based on Artificial Intelligence Algorithms
Bibliographic record
Abstract
The all-round penetration of artificial intelligence technology has brought about a drastic change in the educational landscape, and the teaching system of colleges and universities relies on artificial intelligence technology to expand its own boundaries, leading to interdisciplinary knowledge fusion between dual colleges and universities.With the support of AI technology, a teaching system design idea of interdisciplinary knowledge integration is proposed, and a teaching innovation system of interdisciplinary knowledge integration between dual colleges and universities is established.Taking the learners' interdisciplinary knowledge point response situation as an entry point, input modeling is carried out for the learners' interdisciplinary knowledge points, forgetting coefficient, etc., and the dual colleges' interdisciplinary knowledge tracking SA-BiGRU model is established by combining BiGRU and the attention mechanism, and simulation verification is carried out to verify its effectiveness.Taking a vocational college in province G as an example, a dual college interdisciplinary teaching comparison experiment was designed in combination with the teaching innovation system, so as to verify the effectiveness of the interdisciplinary knowledge integration teaching innovation system.The results show that the AUC and ACC of the SA-BiGRU model can reach up to 0.837 and 0.841 respectively in interdisciplinary knowledge tracking, and the learners' interdisciplinary knowledge reserve and ideological literacy level have been improved by 1.36 and 1.82 points respectively compared with that before the experiment.Relying on artificial intelligence technology can promote interdisciplinary knowledge integration, provide a new research direction for the development of interdisciplinary intelligence in BiGR, and lay the foundation for the cultivation of highly skilled and qualified applied talents.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".