Research on Personalized Learning Resource Recommendation Method for Students Supported by Artificial Intelligence
Bibliographic record
Abstract
With the rapid development of artificial intelligence technology, the education sector is undergoing unprecedented changes.Personalized learning has become a key method to enhance teaching quality and learning outcomes.This paper aims to explore the application of artificial intelligence technology in personalized learning resource recommendation for students, by constructing user profiles, multidimensional models, and personalized recommendation algorithms, in order to provide precise learning resource recommendations for students.This paper proposes a personalized learning resource recommendation algorithm based on a one-dimensional convolutional neural network (1D-CNN).The algorithm first extracts local features of the sequence through convolutional operations, then uses pooling operations to extract long-term features of the sequence, and combines the two features through weighted addition to obtain the user feature information, which allows for the comprehensive extraction of both local and long-term features.Subsequently, the user feature information is multiplied by the linearly transformed sequence information to introduce temporal information.Additionally, student learning records, class performance, and incorrect question records are collected and integrated as user feature information.These user features are passed through a feedforward network to achieve nonlinear transformation and cross-dimensional interaction enhancement.Finally, the user feature vector and item feature vector are computed to obtain their relevance, which is then used for recommendations.Experimental evaluations validate the effectiveness and feasibility of the proposed method, with the aim of providing valuable insights for educational reform and development.
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How this classification was reachedexpand
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 | 0.008 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".