Design of DSLIs Based on Virtual Reality for Deaf Students
Bibliographic record
Abstract
This study aims to design a Digital Sign Language Interpreter (DSLI) based on Virtual Reality to assist deaf students in comprehending lecture material.The research employs a Research and Development (R&D) approach, encompassing four stages: (1) preliminary study, (2) data analysis, (3) product development, and (4) product validation.The development utilizes the Media Development Life Cycle (MDLC) method, consisting of five stages: concept, design, material collection, assembly, and testing.The results indicate that the DSLI application is highly feasible for extensive use by deaf students in lectures, based on evaluations from experts and users.This application represents a promising tool to enable deaf students to access lecture materials without a sign language interpreter's presence.However, the current version is limited to prerecorded sign language content prepared by interpreters, as it does not support real-time translation of lectures.While the DSLI demonstrates significant potential, further improvements are necessary to enhance its real-time functionality and broaden its practical application in classrooms.This study contributes to developing innovative solutions for inclusive education, providing an alternative for deaf students to engage in academic settings more independently.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".