The Integration of Technology with UDL and RTI in Inclusive Classrooms
Bibliographic record
Abstract
The transition to inclusive classrooms in Ontario meant classroom environments had to adapt to the needs of students instead of students being expected to adapt to a standardized curriculum (Parekh, 2018). Although challenges existed in the implementation of this student centered approach, some teachers addressed these obstacles through the use of technology, Universal Design for Learning (UDL) and the Response to Intervention (RTI) frameworks. The transition to inclusive classrooms in Ontario meant classroom environments had to adapt to the needs of students instead of students being expected to adapt to a standardized curriculum (Parekh, 2018). Although challenges existed in the implementation of this student-centered approach, some teachers addressed these obstacles through the use of technology, Universal Design for Learning (UDL) and the Response to Intervention (RTI) frameworks. This paper combined two studies which included both teachers' and students' perspectives of inclusive classrooms. The primary study examined the instructional practices of eight elementary school teachers who experienced successful transitions to inclusion in bricks and mortar and virtual classrooms. The second study explored the experiences of students with and without disabilities who participated in virtual learning during the COVID-19 pandemic. Through online interviews and classroom observations, the teachers demonstrated how technology could increase student engagement, differentiate instruction, and provide students with alternative instruction and assessment methods. However, inconsistencies were revealed in screening approaches to identify the needs of students and monitor students' progress. The students engaged in multiple options of learning with some experiences more positive than others. The paper concludes with a summary of technology-based inclusive practices shared by teachers and students.
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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 | 0.013 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.018 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.003 | 0.016 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".