Academic Achievement of Children with Deafness or Hard of Hearing (DHH) Inclusive Educational Setting: A Scoping Review
Bibliographic record
Abstract
Education for children who are deaf or hard of hearing (DHH) has resulted in various outcomes. In the move to inclusivity, scarce studies have been conducted to evaluate the academic achievement of DHH children, especially in the inclusive education (IE) setting. Therefore, this review aims to map the literature about the academic achievement of children with DHH within IE settings worldwide, including the assessment tools and interventions received. Data were searched from five electronic databases: EBSCOhost [Academic Search Complete (ASC), MEDLINE and CINAHL], Science Direct, SCOPUS, PubMed (PMC) and ERIC (Education Resources Information Centre). Six studies were found to fulfil the inclusion criteria: investigated the achievement of students with DHH academically and in inclusive educational settings. These studies were organised based on the Problem-Intervention-Outcome Meta-Model (PIO MM) conceptual model. In this model, the problem (population of children with DHH) with the intervention [hearing device(s) used, communication mode, classroom type, and therapy received] and outcomes (academic achievement) were analysed. The resulting studies were conducted in Taiwan, the United States, the Netherlands, and Canada. This study described academic achievement using different tools. This review also showed that most of the studies focused on students with a cochlear implant(s) who usually had severe to profound hearing loss. Communication had been rated as a lower achievement by the classroom teachers and formal examinations. With the mapped findings from the scoping review, future research could focus on various degrees of loss, the hearing devices used, and their relationship to educational outcomes, especially in the IE setting.
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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 | 0.006 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.012 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".