Role of Achievement Motivation and Metacognitive Strategies Use for Defining Self-Reported Language Proficiency
Bibliographic record
Abstract
The present study aims to explore the role of achievement motivation and metacognitive strategies for defining self-reported language proficiency in the context of English as a Second Language. Moreover, the study also investigates the complex relationship that exists between motivation, metacognitive strategy, and self-reported language proficiency as they have recently been identified as key predictors of language proficiency. The present research delves into the ways motivation and metacognitive strategies help learners in acquiring self-reported language proficiency. Further, it highlights the skills that can be targeted by using these strategies. The study indicates that enabling learners with positive attitudes, motivation, and metacognitive strategies can have a constructive effect on learning. To determine the important role, achievement motivation and metacognitive strategies play in defining self-reported language proficiency, the study collects responses from 113 participants who will complete three Questionnaires (one each) on self-reports of language proficiency, metacognitive strategies, and achievement motivation. The measures of Achievement Motivation, Metacognitive Strategies, and Self-reported scores of English language proficiencies (skill-wise) will be collected through the respective instruments, Deo-Mohan Achievement Motivation Scale (n-Ach), Metacognitive Reading Strategies Questionnaire (MRSQ), and Self-Reported Language Proficiency Scores.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".