Challenges to Studying English Literature by the Saudi Undergraduate EFL Students as Perceived by Instructors
Bibliographic record
Abstract
Studying English literature is interrelated to studying English as a foreign language (EFL), and thus incorporating literary texts into EFL learning curricula is important for providing EFL learners with the necessary language skills and emotional growth. However, EFL learners prefer to avoid studying English literature due to several challenges that may extend from difficulties inherited in literature itself to the learning and instructional processes. Therefore, this study aimed at investigating the reasons that may discourage EFL learners to study English literature as perceived by their instructors. The sample of this study consisted of 20 English instructors at one of the northern Saudi universities. Two instruments: a survey and a semi-structured interview developed by the researcher were employed to collect the data. Descriptive statistics and qualitative methods were employed to interpret the gathered data. The findings revealed that there were six main different types of challenges that played an important role in the phenomena under investigation, namely: a) literature inherited difficulty, b) learners' cultural misperceptions, c) learners' negative attitudes, d) learners' intrinsic demotivating factors, e) unfamiliarity/ learners' poor prior knowledge, and f) instructional difficulty. Implications for addressing these problems were included.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".