INTERACTION BETWEEN TEACHERS AND DEAF STUDENTS: A SYSTEMATIC REVIEW OF THE LITERATURE
Bibliographic record
Abstract
The aim of this study was to conduct a systematic literature review on the interaction between teachers and deaf students in Brazil and Portugal. The search was limited to research carried out in the last decade (2013-2023) in the following databases: Annual Reviews, DOAJ, Scielo, Scopus, Web of Science and Willey Interscience. The following search terms were used: teacher, student, deaf and interaction. 15 studies were selected that met the inclusion and exclusion criteria. Among the studies selected, the first author to be associated with Brazilian institutions (n=14) was in a higher number, with only one study published in Portugal (n=01). Twelve studies (n=12) used qualitative research, and the majority of studies (n=06) were based on the legal, ontological, epistemological and pedagogical principles advocating inclusive education. Although few studies (n=05) point to the positive potential of interaction between the teacher and the deaf student, this is an aspect that needs more attention as we still need to provide quality education to these students who are already enrolled in classes and thus guarantee them the realization of their rights as learners. Overall, we conclude that the majority of the studies (n=10) indicate limitations, barriers and obstacles that hinder the interaction between the teacher and the deaf student and consequently affect their learning. Article visualizations:
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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 | 0.016 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.023 | 0.020 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".