Motivation’s Influence on Strategies for English Learning and Improving
Bibliographic record
Abstract
Autonomous study emphasizes the learner s initiative, enthusiasm and creativity. In all fields of education, there is growing emphasis on “learner一centered” teaching methods and the ability of learner autonomy .Many experts and scholars have found that learning strategies plays an important role in English language learning, but the importance of affective strategy use in English learning is often ignored by people. Therefore, this paper focuses on the frequencies of affective strategies use in English learning and their relationships so as to enable college students to use positive affective strategies effectively to improve their autonomous learning ability. The article also focuses on the relationship between motivation and English learning, the influence of motivation on English learning(That is, English learning motive may be simply viewed as the reason of learning English; different motives will lead to different learning methods ; generally speaking, surface motive does not endure longer than deep motive.;strong motivation can lead to final success) and six strategies of improving English learning(That is, developing proper attitudes towards English learning and letting students know the pressure of it; goal and feedback; praise and criticism ;contest and cooperation; expectation and appraisement; achievement motive).
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".