Effects of Illustration Types on the English Reading Performance of Senior High School Students with Different Cognitive Styles
Bibliographic record
Abstract
Illustration is always used as an example to make the written text or the utterance more clear in general. In Winarski’s opinion (1997), one picture equals thousands of words. That is to say, illustrations are capable to express the meaning of unfamiliar language or a great deal of information in the reading material by vivid pictures, tables, drawings, paintings and so on. As a result, illustrations are applied to many different fields including English language teaching. Based upon Song’s 3 types of illustration classification (2005), decorational illustrations, explainable illustrations and promotive illustrations, this paper tries to investigate the effects of illustrations on the reading performance of senior high school students with different cognitive styles (field-dependence, field-mix and field-independence) in the process of English reading. The result shows that: 1). There is a significant correlation between illustration types and reading performance in terms of field-dependent students. The coefficient of explainable illustration to reading peformance is the highest, while the lowest coefficient is decorational illustration. 2). As for field-mixed participants, their reading performance is also closely associated with illustrations. However, the coefficients are lower than that of field-dependent participants. Decorational illustration has no obviously relation to reading performance. Explainable illustration also reaches the highest coefficient, and it can better improve student’ reading score than promotive illustration. 3). Speaking of field-independent students, no correlation has been found between decorational, promotive illustration and reading performance. However, there exists a significant correlation between explainable illustration and reading performance for field-independent participants.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".