Using Novels in the Language Classroom
Bibliographic record
Abstract
The first goal of this quantitative and qualitative study was 1) to look at students' perceptions of reading novels in EFL classes, 2) to learn what teachers think about the benefits of teaching novels in language classes, 3) to determine whether reading novels may inspire students, broaden their cultural awareness, and increase their language proficiency, and 4) to pinpoint potential difficulties that students might encounter while studying novels. The samples included 24 English professors who are currently teaching novels at chosen universities in Myanmar, together with 71 third- to fourth-year English specialized students. They were chosen using the purposive sampling technique. The data were gathered via a questionnaire and semi-structured interviews with a few teachers. Percentage, mean, and standard deviation statistics were used to assess the quantitative data. The results indicated that students' opinions toward reading novels in EFL lessons were favorable. The teachers' conversation showed several difficulties and benefits of using novels. The results have instructional value for EFL instruction since they show how well-received a novel was in an EFL class, the benefits it offered, and the difficulties it presented with reading.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".