The Effect of Frequent Dictation on the Listening Comprehension Ability of Elementary EFL Learners
Bibliographic record
Abstract
This study investigated the effects of frequent dictation on the listening comprehension (LC) ability of elementary EFL learners. Two homogeneous groups of elementary EFL learners at the Kish Language Institute in Tehran, Iran were chosen. Each group consisted of30 male elementary EFL students, 20 to 35 years of age. All the participants had had the same amount of exposure to listening materials before the experiment, and all had studied English for three terms (each term consisting of 20 sessions of 100 minutes each) at the Kish Language Institute. One of the groups was chosen as the experimental group, and the other as the control group. For one term, consisting of 20 sessions, the students in the control group were given the listening exercises in their textbook, Headway Elementary (Soars & Soars, 1993). The experimental group, in addition to the listening exercises in the textbook, were given dictation 11 times during the term. At the end of the term the Le ability of both groups was posttested by a battery of 40-item NCTE Elementary Listening Tests (National Council of Teachers of English, 1972)/ which was also used as the listening pretest. The results showed that dictation had a significant effect on the listening comprehension ability of the participants in the experimental group. The mean gain scores of the experimental group were significantly higher than those of the control group.
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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 | 0.002 | 0.017 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".