Designing inclusive tech playful educative solutions for visually impaired learners in STEM education
Bibliographic record
Abstract
Abstract Education in science, technology, engineering, and mathematics (STEM) is essential to achieving continued technological advancement. The most critical years for instilling knowledge are during childhood, and a strategic way to accomplish this is through playful materials. Therefore, there is a need to develop more inclusive solutions to achieve the good inclusion of visually impaired (VI) learners in this learning area. Despite their importance, specific design guidelines are scarce for developing playful, educational solutions for VI learners in STEM. Qualitative research was conducted through interviews and observations of the interactions between VI Learners playing an audio game and tactile 3D printed blocks, which covered an age range of young participants aged 8–18 years and adults aged 30–40 years in Taiwan. Surprisingly, the results showed that the combination of tangible and audio elements for playful purposes opens the way for students to show interest during educational interactions and, at the same time, allows them to understand the concept, especially when presented in different game missions but repeating the same principle/concept. In conclusion, there is a need for more inclusive strategies and approaches for playful STEM tools for VI learners, and one important aspect of achieving this is design guidelines. This study aims to understand the educational context of VI learners and their interactions when playing with educational materials to learn STEM concepts and develop design guidelines for the future development of playful STEM educational games.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".