Factors Affecting the Participation of the Deaf and Hard of Hearing in e-Learning and Their Satisfaction: A Quantitative Study
Bibliographic record
Abstract
Today the digital divide is measured not so much by access to the internet but by people's ability to interact in cyberspace, which calls for specific digital communication skills. These skills are also required for participation in e-learning, bearing in mind that the deaf and hard of hearing have certain characteristics related to their condition. This study aims to identify inequalities in access to e-learning by those who are deaf or hard of hearing, observe how they are related to social and demographic factors, and confirm whether the accessibility of the e-learning environment has a direct influence on their satisfaction. The study has been conducted via an electronic survey in Spain with a non-probabilistic sample, in line with the characteristics of this sector of the population, recording social and demographic data, and information about the participants' experience of e-learning and their satisfaction with the experience. The representative survey sample consisted of 484 deaf and hard-of-hearing individuals in Spain, aged between 16 and 64. Using a chi-square test for independence we obtained a significant relationship between participation in online courses and the following variables: level of education, knowledge of sign language, and number of members in the household. A correspondence analysis was then carried out to detect particular relationships between classes of the related variables.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".