Role of Achievement Motivation and Metacognitive Strategies Use for Defining Self-Reported Language Proficiency
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it