Skillful Analysis of English Language Lexical Processing and Language Cognitive Learning Assisted by Artificial Intelligence
Bibliographic record
Abstract
With the development of artificial intelligence technology, the learning mode of "artificial intelligence + education" has become the direction of the times.Through a questionnaire survey on students' vocabulary learning strategies and taking students of a middle school as the research object, the study explores the level of strategy use in English vocabulary learning in terms of the frequency of strategy use and the differences in strategy use among students of different levels.On this basis, the way of English word sense processing with the assistance of artificial intelligence is summarized and the word association memory model is proposed.And two classes in a middle school are selected for teaching experiments to apply the word association memory model to English vocabulary learning and explore the effect of the model on students' word memory.Overall the cognitive strategy (3.489) and resource strategy (3.477) of English vocabulary learning are used more frequently.The English vocabulary level model of the students in the experimental class increased after the teaching experiment, which was 8.05 points higher than that of the control class and still 5.118 points higher than that of the control class in the delayed test, reflecting the vocabulary learning effect and durability of the word association memory model.Students can improve their language cognitive learning skills in three aspects: metacognitive strategies, cognitive strategies, and communicative/influential strategies, which further promote the development of English proficiency.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".