Bibliographic record
Abstract
typical classroom in Ontario is filled with a variety of learners with diverse needs. These various needs require teachers to differentiate instruction or create a universal design for learning (UDL) so that all students can participate. As a result, research is needed to explore and describe successful programs that can support all learners. One way to do this is to develop pedagogical practices for atypical learners and examine how these could be broadened for more typical learners. This research examines a series of general music lessons, including singing, playing percussion instruments and musical games, for atypical twelve-year-old learners. An Action Research methodology was used to examine six weeks of lessons taught to three students by the primary researcher. Data were collected using reflective journals, portfolios and videos of the sessions. Thematic analysis was conducted to examine similarities and differences in learner profiles, trends in the content of the lessons and pedagogical development over time, as well as to define some strategies or activities that could form the basis of a UDL approach. Despite the students’ atypical learning profiles, only minor accommodations were required during lessons. Overall, this research demonstrates the value of a pedagogical approach that articulates learning goals while allowing the path to achieving those goals to be different for each student, reinforcing the importance of the UDL approach. Moreover, the action research methodology highlights the importance of incorporating opportunities to work with atypical students in music teacher education, so that future teachers can develop a UDL approach.
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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 | 0.017 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.009 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.005 |
| 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".