Challenges of Deaf and Hard-of-Hearing Students in English Learning in Higher Education
Bibliographic record
Abstract
Following the trend of inclusive education in China during the past decade, a growing number of deaf and hard-of-hearing (DHH) students have entered regular tertiary institutions. Learning English as an additional language presents challenges to these DHH students and relevant research is underdeveloped. To explore the English learning experience of DHH students in regular higher education in China, this research conducted a qualitative case study of three DHH students currently enrolled in regular tertiary education. The research findings show that DHH students face multiple difficulties in English learning including inappropriate instruction and assessment methods, insufficient accommodations and support, and a non-supportive physical learning environment. These factors result in a low level of inclusion in the higher education system. The insights from this research contribute to the ongoing global conversation of inclusive education. It offers a comparative view for Western language educators in developing inclusive English education for DHH students.
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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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.009 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".