Students’ Evaluations of Multilingual Educational Slides and Their Visual Attention Distribution on Slides with Different Layouts
Bibliographic record
Abstract
Following efforts to promote internationalization at academic institutions, the use of multiple languages on educational slides (e.g., PowerPoint) has gradually increased. Multilingualism in learning has its advantages, but having multiple languages on educational slides can lead to crowding and cognitive overload. To investigate how students perceive multilingual slides, evaluations were gathered from Japanese (N = 20) and Indonesian students (N = 20) during an eye-tracking experiment in which their visual attention distribution on the slides was assessed. The slides contained text in three languages (English, Japanese, and Bahasa Indonesia) and were varied according to their layout. One group watched slides with text separated in blocks, with one text block for each language, while the other group watched slides consisting of a single, mixed block with each sentence describing the same information in a different language. The students’ evaluations showed that slides with a mixed layout were judged as more crowded and required more processing effort than slides with a separated-block layout. Furthermore, while the students dwelled their gaze significantly longer on text in their native language (either Bahasa Indonesia or Japanese) on separated-block slides, for slides with a mixed layout, the gaze patterns did not significantly differ between languages. The results of a comprehension quiz taken after the slide presentation, however, showed that students performed better after having watched the slides with the mixed layout. Thus, although judged as more crowded and requiring a wider attention distribution, slides with a mixed layout may be preferable in multilingual education.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".