Reading Strategy and Eye Movement of Japanese Students When Reading English as a Foreign Language
Bibliographic record
Abstract
This study reports on the relationship between reading strategies and eye movements observed when Japanese students attempt to comprehend English texts. Reading texts in an unfamiliar foreign language or with specialized content is generally difficult; therefore, computer support is essential. This study aims to classify and identify readers’ characteristics to customize support style. We focus on eye movements and reading strategies as readers’ characteristics, which depend on readers’ language ability and knowledge. In the experiment, we examined the reading strategies of Japanese readers of English using a questionnaire and factor analysis. We discovered two typical reading strategies: context-guessing and word-wise translation strategies. We also examined the effects of various reading strategies on eye movement and discovered significant differences in several eye movement features. We used a support vector machine to recognize the readers’ reading strategy based on the features of eye movement. The discriminator estimated the reader’s reading strategy with an F value of 0.88. Based on the results of this experiment, we expect this discriminator to help us understand readers’ reading strategies and support efficient reading comprehension strategies.
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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 | 0.003 |
| 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) | 0.001 | 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".