Developing Lexical Competence Through Literature: A Study of Intermediate Students of Pakistan
Bibliographic record
Abstract
This study brings to light the fact how much teaching English through literature renders any pay off in developing and honing the EFL/ESL learners’ lexical competence. This study strives to investigate the role of literature in developing the ESL/EFL learners’ lexical competence, find out the ESL/EFL learners’ attitude towards teaching lexical competence through literature, know the lexical competence level of the ESL learners, examine ESL/EFL learners’ vocabulary knowledge and get insight into the difference between the ESL/EFL learners’ receptive and productive knowledge of vocabulary. In the Pakistani context, literature seems to be inadequate language teaching tool at HSSC level. To achieve the set objectives, the researcher went for the quantitative research methodology. So, a questionnaire comprising of 15 items encompassing the different aspects of vocabulary was designed to collect data from 600 subjects (male/female) of intermediate level. The researcher has also conducted “Vocabulary Level Test” and “Word Associate Test” as achievement tests. The collected data were analyzed through software package (SPSS XX). The findings of this study explicitly reveal that the EFL learners remain unable to develop lexical competence when they are taught English through literature. This study recommends that the teaching of English should be application oriented and task-based strategies and activities should be resorted to by the EL educators.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".