Teaching Mathematics to Students with Hearing Loss Using Instructional Materials
Bibliographic record
Abstract
Submission of an article implies that the work described has not been published previously except in Teaching mathematics to students with hearing loss requires special attention and care. Using materials in mathematics education is crucial in helping students concretize abstract concepts and relationships, create schemas related to the taught subject, understand the content, apply it, and develop mathematical thinking in a broader context. In mathematics classes for students with hearing loss, teachers often struggle to find ready-made materials suitable for the instructional content and the language, cognitive, and readiness levels of students with hearing loss. Teachers themselves need to prepare instructional materials for students with hearing loss. This study aims to identify and explain the types and characteristics of materials prepared and used by teachers in mathematics classes for 6th-grade students with hearing loss, and to highlight the contributions of these materials to the mathematics teaching process. Research data were collected through materials used in mathematics instruction, lesson plans and evaluations, the researcher's diary, and video recordings of lessons. The collected data were analyzed by using the content analysis method. As a result of the research, support and resources have been provided to teachers and instructional programs to create and use mathematics lesson materials suitable for students with hearing loss.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 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".