Teachers' perceived usefulness of assistive technology in Ontario classrooms
Bibliographic record
Abstract
Purpose Despite the positive impact that assistive technology (AT) can have on the academic success of students with learning disabilities, it is often inconsistently implemented or abandoned. It has been established that teachers' perceived usefulness of AT can act as a barrier to classroom AT implementation. The purpose of this study is to expand the current understanding of the challenges with implementation of AT within the classroom environment to inform teacher training on AT tools, improve professional development around AT and address the systemic and practical barriers that impact AT implementation within Ontario classrooms. Design/methodology/approach This research examined Grade 6–10 Ontario-certified teachers' (N = 111) perceptions of AT and the variables that predict perceived usefulness of AT. The study used a mixed methods design including a survey consisting of open- and closed-ended items that elicited information about teachers' AT knowledge and training, their access to AT resources, their perception of administrative support for access to and implementation of AT, the usefulness of AT and the barriers to AT use in the classroom. Findings An exploratory linear regression was conducted to predict perceived usefulness of AT from AT training, AT resources and AT knowledge and revealed that AT resources and AT knowledge added statistically significantly to the prediction, whereas AT training did not. A thematic analysis of open-ended survey responses and interview data further identified that access, training, Internet and student motivation may influence AT use. Originality/value Implications for teachers’ AT training and provision of AT resources are discussed.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".