The Effects of Text Length and Picture on Reading Comprehension of Iranian EFL Students
Bibliographic record
Abstract
This research examined the effects of text length and picture on reading comprehension. ‘Dual Coding Theory’ is a powerful rationale behind using pictures and texts together. According to this theory, “information is much easier to retain and retrieve when it is dual-coded because of the availability of two mental representations instead of one.” The sample consisted of 79 first grade female students from three high schools in Iran. The participants were of the same level of proficiency. The researcher utilized two texts in this study. In fact, one of these two texts was a shortened version of the original text. Using a between subject design, participants were divided into four groups. Each group read a reading comprehension text under one of four conditions: 1) long text with picture, 2) long text without picture, 3) short text with picture, and 4) short text without picture. The reading comprehension texts were accompanied by 5 multiple-choice items and 10 true-false items. Although the participants performed better on the original text, the results of the analysis of variance (ANOVA) showed that length had no significant effect on reading comprehension of Iranian high school students. However, subjects performed better on texts with picture. Therefore, picture is a key variable in influencing EFL students’ reading comprehension at high school levels. These findings have pedagogical implication in the EFL and ESL fields.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".