Integrating Technology With Instructional Frameworks to Support all Learners in Inclusive Classrooms
Bibliographic record
Abstract
In Ontario, as the number of students requiring special education support continues to rise, the transition to inclusive classrooms has become more challenging for teachers due to limited time and lack of resources and support in the classrooms. However, this study explored how eight elementary school teachers addressed these obstacles in their successful transitions to inclusion through the integration of technology, Universal Design for Learning (UDL) and the Response to Intervention (RTI) frameworks in both online and physical classrooms. Through online interviews and classroom observations, the teachers orally shared and demonstrated how technology could increase student engagement, differentiate instruction, provide students with alternative instruction and assessment methods, and build teacher capacity within the classrooms. Despite this successful integration of technology and instructional frameworks, inefficiencies were revealed in screening approaches and teachers’ access to streamlined assessment resources to identify the needs of students. A discussion examined the teachers’ barriers in supporting the needs of all learners with proposed technology-based considerations that may assist other teachers in their transitions to inclusive classrooms.
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.012 |
| 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".