The Effect of Content-Based Approach in Student vs. Teacher-Centered Environment on Improving Speaking among Iranian Intermediate EFL Learners
Bibliographic record
Abstract
Speaking is the most important skill of language. Speaking a language is especially difficult for foreign language learners because effective oral communication requires the ability to use the language appropriately in social interactions, and in suitable context and situation. Purpose of this study was to investigate the relationship between Iranian intermediate EFL learners who learn speaking based on content approach in student-centered environment and their learning in the traditional teacher-centered environment. To this end, there are two classes, one as the experimental and the other as the control one. The same teacher taught them both. The subjects were all homogenous regarding their age, gender, and first language. During the experiment, the experimental group, who were in the environment of content-based approach in student-centered, was received quizzes. The results indicated that a statistically significant positive relationship existed between giving the students the central role in content-based approach class and yielding in better performance of speaking ability. The results revealed the positive relationship between content-based approach in student-centered class and the improvement of EFL Iranian students’ speaking ability. Preparation of such a system for teaching speaking to learners, learning English as a foreign language will facilitate the teaching and learning process for both teachers and learners.
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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 | 0.000 | 0.002 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".