Effects of Integrating Literary Texts in Enhancing the Quality of Academic Writing of University Students in Bahrain
Bibliographic record
Abstract
The research explored the effects of integrating literary texts on the writing performance of selected university students. It also documented the attitudes of the university students on the integration of literary texts in their regular writing course and their perceptions on the difficulties they encounter in the writing process. These research objectives were rationalized by the need to address the writing difficulties by applying theoretical assumptions and addressing the empirical gaps of previous studies on the effectiveness of literature as a rich resource in developing language competence. The research participants were composed of first year business students who were enrolled in an ESP course in one of the universities in Bahrain. Each group was composed of 35 students with an equal distribution of male and female participants. A mixed method approach was used to address the core research questions with the primary application of an experimental design. The results revealed that literary text integration is effective in improving the academic writing performance of the university student-participants as indicated by the statistical test, wherein the experimental group (m = 3.35) had a higher level of improvement than the control group (m = 2.93) in terms of their overall writing performance after the intervention. The student-respondents had a positive attitude towards the integration of literary text in their regular writing course. Writing difficulties included the process of writing the introduction, body, conclusions of their academic writing tasks, use of relevant vocabulary communicative achievement, organization, and language use. It was recommended that language teachers should incorporate literary texts that are related to the writing lessons. Educational administrators and leaders may revisit the curriculum and use the empirical results in developing a more relevant language curriculum especially in the area of writing instruction where literary texts could be integrated.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".