English Vocabulary Learning Strategies between High-achievers and Low-achievers
Bibliographic record
Abstract
This study intends to compare the vocabulary learning strategies between high-achievers and low-achievers in junior high schools, and thus to explore effective vocabulary learning strategies to provide suggestions for English vocabulary teaching and learning practice. It is found out that high-achievers apply English vocabulary learning strategies more frequently than low-achieving students do. More specifically, in terms of meta-cognitive strategies, low-achievers use more pre-planning strategies, while high-achievers apply more selective attention strategies. For cognitive strategies, both high-achievers and low-achievers tend to use repetition strategies. Achievers are better at categorizing what they have learnt than low-achievers. For affective strategies, low-achievers apply reference books strategies most frequently and neither type of students uses authentic material strategies very often. Students’ beliefs, habits and attitudes towards vocabulary learning affect the application of strategies to some extent. High-achievers are better at utilizing the environment and even creating opportunities for English communication than underachievers. In addition, high-achievers have stronger learning motivation and they are also better at setting and achieving goals than low-achievers.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".