A Personalized Path Generation Model for English Learning Based on Natural Language Processing
Bibliographic record
Abstract
In this paper, based on the knowledge graph, word vectors and other personalized path generation related technologies, based on the graph convolutional neural network to complete the construction of the English knowledge graph model, to generate a personalized English knowledge graph, drawing on the data structure in the graph, to generate a personalized learning path, in order to make the generation of personalized learning path is more reasonable, in accordance with the difficulty value of the exercises for the exercises to be sorted.Simulation experiments are designed to evaluate the difficulty level of the generated exercises.The difficulty level of most of the English exercises generated by the personalized recommendation path is concentrated in the easy and general levels, and there are a total of 2,229 questions in these two difficulty levels, so the difficulty level of the generated questions is moderate.After a period of personalized path-generated English learning, six teaching activities were carried out, and the average score of the first post-test of the experimental group was higher than that of the control group, and the Sig values were all less than 0.05, indicating that the difference in the scores of the two groups of students was significant, which side by side reflected the accuracy of personalized path-generated English teaching.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".