Language Learning Strategies Used by Male and Female Literature Students at Post Graduate Level
Bibliographic record
Abstract
Language learning strategies play an important role in acquiring language proficiency skills at different levels of learning. The current study being quantitative in nature, has been carried out to investigate the role played by language learning strategies (LLS) while learning English literature at post graduate level. Therefore 160 students from three different institutions were randomly selected to participate in this survey-oriented project. An instrument originally created by Oxford (1980–1990) about Strategy inventory of learning a language (SILL) version 0.7 and further modified after piloting was distributed among the students. The findings of the sturdy reveal positive relation of strategies among the students while the females surpassed over male participants in adopting strategic use of learning. Obtained data show that there was no significant similarity found among them; but lot of differences observed regarding use of different strategies. The females were most frequent users of memory, cognitive, affective, meta-cognitive and social strategies while male learners were involved in using compensatory, cognitive and also the active users of meta-cognitive strategies. As far as difficulties during learning English literature are concerned: a lot of major aspects of language learning strategies were observed that need to be solved. On the whole, students at postgraduate level are aware of LLS and utilize these strategies while learning English language and literature.
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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 | 0.002 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".