Research on Design and Optimization of Personalized Network Education System Based on Artificial Intelligence
Bibliographic record
Abstract
This paper studies the design and optimization of a personalized network education system based on artificial intelligence (AI). A personalized network education system with hierarchical micro-service architecture is designed. The core technology stack includes Spring Cloud, Docker, React, Secondary, TensorFlow Serving, etc. The system provides accurate learning support for students through core functional modules such as user portrait, knowledge recommendation, path planning, intelligent question answering and early warning of learning situation. The adaptive recommendation engine adopts hybrid recommendation algorithm, combining collaborative filtering and knowledge map embedding, and dynamically adjusts the weight through reinforcement learning to optimize the recommendation effect. Learning path planning uses reinforcement learning to optimize path generation, ensuring that the response time is less than 200ms. Intelligent question answering is based on BERT and BiLSTM, and the problem solving rate is 92%. In the early warning of academic situation, LSTM time series prediction combined with SHAP analysis is used to predict the risk of failing the course three weeks in advance. In the aspect of system optimization, performance bottlenecks were found through stress testing and code analysis, and technologies such as GIN index, Vitess sub-database and sub-table, three-level caching strategy, model quantization compression, batch reasoning and knowledge distillation were adopted, which significantly improved the system performance. AB test results show that after optimization, the concurrent carrying capacity of the system is improved by 192%, the recommended response delay is reduced by 75.6%, and the peak CPU usage of the database is reduced to 47%. In addition, the system also predicts learning hotspots by LSTM to realize dynamic cache preheating, automatically selects the model precision according to the user equipment type, and automatically expands and contracts the capacity of Kubernetes based on Prometheus index, and the response time is less than 10 seconds. This study provides a useful reference for the design and optimization of personalized online education system, and helps to promote the development and application of personalized learning system.
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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".