Research on Intelligent Learning Path Planning and Recommendation Algorithm in OMO Teaching Mode Based on Artificial Intelligence Grand Modeling
Bibliographic record
Abstract
OMO teaching mode based on artificial intelligence big model is one of the important future research directions and application landing forms in the future education field.The learning path recommendation algorithm based on big language model is constructed by integrating Transformer architecture, neural network architecture and self-attention mechanism.Combining it with the course knowledge graph, it links the learners with the knowledge system and visualizes the results of the intelligently planned learning path.The study shows that compared with several other algorithms, the personalized learning path recommendation algorithm based on AI big model has better convergence speed and stability.The optimal solution for learning path planning is found after only about 90 iterations.Taking "Chemical Process and Control Simulation" as the target course, the method in this paper gives the learning path and course.Through the questionnaire survey, the mean value of the four dimensions of pre-class pre-study, classroom exploration, post-class enhancement, and learning satisfaction is more than 3 points, which indicates that the OMO model and the teaching model of the artificial intelligence big model have a better experience.
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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 | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".