A Quantitative Study of Multiple Regression Modeling in the Improvement of College English Vocabulary Learning Efficiency
Bibliographic record
Abstract
The expansion of English vocabulary is the foundation of college students' English learning and the key to improve English learning.This project centers on the quantitative analysis of college English vocabulary learning ef iciency improvement, through the questionnaire survey to understand the use of English vocabulary learning strategies of students.The in luencing factors of English vocabulary learning ef iciency improvement are selected, correlation analysis is carried out, and then multiple regression model is used to explore the role of each variable on the improvement of English vocabulary learning ef iciency.The results show that students most often use the metacognitive strategy of preplanning (3.674), and that students who are good at learning are more inclined to adopt the metacognitive strategy to control vocabulary learning from a macro perspective.Multiple selfelements and environmental elements together positively affect the improvement of English vocabulary learning ef iciency (p < 0.01), with the most signi icant effects of learning strategies (0.482), teaching methods (0.457) and learning strategies (0.416).It is recommended to promote the ef iciency of English vocabulary learning through innovative teaching methods, combining word class memorization, expanding the scope of reading, and vocabulary association learning.
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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 | 0.028 | 0.114 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".