The Effect of Implimentation of TBLT in Reading Comprehension Classes of Iranian EFL Learners
Bibliographic record
Abstract
The present study investigates the impact of Task-Based Language Teaching (TBLT) on Iranian EFL learners’ reading comprehension performance. 70 participants were assigned randomly to the experimental and control groups. Having instructed the two groups with the same texts but different task types and activities (i.e. tasks in 4 types) during 20 sessions, the learners’ reading performance results were compared through utilizing a reading post–test to both groups. The obtained data was analyzed using t-test to examine the effects of independent variable, namely, the method of teaching reading (task-based activities vs. classical reading comprehension) on learners’ reading performance as the dependent variable. As well, the performance of the experimental group in four task types was analyzed in order to investigate possible differences among four reading sets of scores obtained on four task types. A follow up TUKEY test was also conducted to locate the exact areas of difference. Results showed that TBLT had a significantly more positive effect on learners’ reading performance compared to traditional reading instruction. Also, the second task type investigated in this study, namely read, note and discuss, found to be more useful in increasing learners’ reading skill. This study has pedagogical implications for Iranian teachers, because reading comprehension is a rally important part of most standard exams in this country. Yet due to limited participants, it requires further studies in future.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".