Narratives of teacher-student relationships : how itinerant teachers of the deaf and hard of hearing support their students’ social and emotional development
Bibliographic record
Abstract
Positive teacher-student relationships promote healthy school experiences and have been shown to play an important role in creating positive social and academic outcomes for students, including students with special learning needs (e.g., Hamre & Pianta, 2001). Most deaf and hard-of-hearing (DHH) students are educated in inclusive school environments alongside their hearing peers, and likely receive additional support from an itinerant teacher of the deaf and hard of hearing throughout their school years (kindergarten to grade 12). However, very little is known about the significance of this unique teacher-student relationship in terms of social and emotional support, nor in what ways this relationship may help or hinder social inclusion at school. To address the paucity of research in this area, I used a narrative inquiry and multiple case study design to examine the characteristics of the itinerant teacher-DHH student relationship. Each participant (four itinerant teachers and four DHH students) participated in two separate individual interviews and was asked to reflect upon their relationship working with DHH students or itinerant teachers, as appropriate. The first interview was semi-structured and captured the participants’ perspectives of their itinerant teacher-DHH student relationships generally. The second interview focused on the meaning and significance of the itinerant teacher-student relationship. Narrative stories for each participant were written from the interview data and analyzed using a constant comparison, thematic content analysis (Braun & Clarke, 2006). Six prominent themes emerged from the itinerant teacher narrative stores: identity development (of students), attachment, safe space, connector, advisor, and itinerant teacher identity. Five prominent themes emerged from the DHH student narrative stories: identity development (of students), attachment, safe space, connector, and advisor. This study contributes to the field of Deaf Education in terms of identifying possible important aspects of the itinerant teacher-student relationship from both the teachers’ and the students’ perspectives. In addition, the findings shed light on potential interpersonal mechanisms that may be involved in creating successful school experiences for DHH students who are educated in inclusive school environments.
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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 | 0.009 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.008 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".