Integrating Reader-Response–TBLT: Short-Story-Driven Gains in Vocabulary Depth, Inferencing, and Engagement among Saudi EFL Learners
Bibliographic record
Abstract
This study examines the integration of Reader-Response Theory (RRT) with Task-Based Language Teaching (TBLT) to develop Saudi EFL learners’ vocabulary depth, inferencing ability, and engagement through short-story reading. Using a mixed-methods design, 120 male university students were assigned to an experimental group receiving Reader-Response-based TBLT instruction and a control group following conventional reading tasks. Quantitative data from pre- and post-tests measured gains in vocabulary depth and inferencing, while qualitative reflections and classroom observations captured cognitive and affective engagement. The results showed that the experimental group achieved significant improvement in vocabulary knowledge and inferential reasoning. Students also demonstrated greater willingness to interpret texts, discuss ideas, and negotiate meaning collaboratively. Their reflections revealed deeper engagement with language and increased awareness of lexical use. The findings suggest that integrating RRT within a TBLT framework supports both linguistic development and critical reading in Saudi EFL contexts.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".