The tiered approach to support all learners in inclusive classrooms
Bibliographic record
Abstract
The aim of the inclusive education strategy in schools in Ontario is for “all students to participate in the education program in a common learning environment with support to diminish and remove barriers and obstacles that may lead to exclusion” (Inclusive Education Canada, 2017, par. 3). This means not just placing students with special education needs in mainstream classrooms, but ensuring they receive the support they need to access the curriculum in the way they learn best. This qualitative case study explores how teachers are using technology and Universal Design for Learning (UDL) to identify learning needs, implement instructional strategies and monitor student’s progress in the Response to Intervention (RTI) tiered approach. Due to the impacts of learning during the pandemic, I investigated the instructional practices of eight elementary school teachers in bricks and mortar and virtual inclusive classrooms. From the data two critical areas for successful inclusion were identified: 1) teachers’ capacity for inclusive teaching and 2) student needs. Within the area of teacher’s capacity, four themes impacting capacity either positively or negatively emerged: 1) opportunities for technology training; 2) availability of inclusive education training; 3) building student-teacher partnerships; 4) sharing responsibilities through teacher-teacher partnerships. An additional eleven themes unfolded that related to the needs of students: 1) building student relationships; 2) using inconsistent assessment tools and methods; 3) identifying instructional needs; 4) creating opportunities to increase student engagement; 5) increasing engagement with collaborative learning through technology; 6) accommodating student’s needs with an Individual Education Plan (IEP); 7) differentiating instruction through the content, process\nand the environment of learning; 8) providing choice of evaluation methods with technology; 9) ensuring differentiation through the product of learning; 10) monitoring the effectiveness of accommodations in the Individual Education Plan (IEP); 11) recognizing the impact of marking and providing feedback on teacher’s capacity. The findings illustrated how teachers combined RTI, UDL and technology to implement successful inclusive classrooms and overcome their initial challenges of time, and lack of training. Based on these findings and the current literature, a summary of practical recommendations to assist teachers in the transition to inclusion has been included in the discussion chapter. A pilot training program has also been proposed with full details in Appendix P and a summary of effective strategies aligned to each component of the Tiered Approach is also included in Appendix Q.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.012 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.007 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.023 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".