Intelligent Algorithm Evaluation of Incidental English Vocabulary Acquisition in Complex Reading Tasks
Bibliographic record
Abstract
Vocabulary is the basis of learning a foreign language, and the cultivation of students' language ability is an effective means to improve students' ability to master vocabulary. In English teaching, how to effectively improve students' vocabulary level is a matter of concern. The main purpose of this paper is to explore how to use intelligent algorithms to analyze and evaluate the effect of incidental English vocabulary acquisition. From the perspective of incidental vocabulary acquisition, this paper further proved that output reading could promote students' English learning and provide some support for its application in practice. Through two groups of immediate tests and delayed tests, it was found that the learning efficiency of Class 1 of output group was higher than that of Class 2 of input group: 42.99>39.09>35.66>28.17, 16.78>14.50>14.49>12.22, 26.50>22.95> 19.32>15.90. In practical application, the study of this paper could not only provide some useful references for senior high school English teaching, but also provide some useful references for students' choice of vocabulary and reading.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".