TEACHERS’ BELIEFS AND TEACHING PRACTICES REGARDING STUDENTS WITH EXCEPTIONALITIES THROUGH THE USE OF TECHNOLGY AND ASSISTIVE TECHNOLOGY IN MAINSTREAM CLASSROOMS
Bibliographic record
Abstract
The present study examined teachers experiences with integrating technology devices in Canadian classrooms to support the learning of exceptional students. A large quantity of existing research speaks to technology integration, the benefits and outcomes that it has on all children. This study aimed to learn the specific technology devices including assistive technologies that are being integrated in today’s classrooms, the challenges that teachers face with technology integration and how they surpass them. This qualitative study was guided by the following question: How is a sample of elementary school teachers utilizing technology devices, including AT devices, in meaningful ways in their classroom to support children with exceptionalities? Overarching themes include the use of communicative and academic technology for students who are non-verbal, and that technology integration is best supported by a collaborative school community. Ultimately, as a beginning teacher, I anticipate to discover the learning practices and learning opportunities that I can create with technology integration, to promote an inclusive classroom regardless of the exceptionality that a student may have.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".