Integrating Process Genre and Task-Based Approaches to Enhance Academic Reading and Writing Skills Among Thai Undergraduates
Bibliographic record
Abstract
This study examines the development and evaluates the effectiveness of an academic model that integrates Process Genre and Task-Based approaches to enhance Thai undergraduate students’ academic reading and writing skills. Recognizing persistent challenges in English academic literacy, particularly in reading and writing, this study employed a hybrid instructional model designed to mirror real-world academic tasks, using IELTS-based assessments to measure proficiency gains. The intervention was implemented among 31 second-year students majoring in Teaching Chinese as a Foreign Language. A pre-test/post-test design demonstrated significant improvement in both reading and writing skills, with the mean reading score increasing from 19.83 (S.D. = 4.94) to 29.74 (S.D. = 3.38) and a large effect size (Cohen’s d = 3.38) and writing scores from 21.59 (S.D. = 5.44) to 29.33 (S.D. = 3.26) with an effect size (Cohen’s d = 3.05). The average reading and writing scores reached IELTS Band 7.0. Participants reported high satisfaction across all measured dimensions, indicating strong engagement and perceived value. Qualitative data from instructor interviews corroborated these findings, highlighting challenges such as vocabulary limitations and structural weaknesses, and underscoring the benefits of structured feedback and explicit strategy instruction. The study concludes that the integrated model effectively promotes academic literacy and offers a scalable solution for EFL contexts.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".