Supporting learners with Down Syndrome using the behavioural phenotype and universal design for learning in inclusive classrooms
Bibliographic record
Abstract
This two-phase study explored teachers’ knowledge, beliefs, and experiences around Universal Design for Learning (UDL), Down syndrome (DS), and the behavioural phenotype of Down syndrome to determine how information about these topics could be shared more effectively with educators to benefit learners in inclusive classrooms. In Phase One, Canadian Kindergarten- grade 7 teachers were surveyed to guide the first revision of a three-part online workshop series intended to support teachers in addressing the identified needs of students with DS using UDL strategies. Phase Two utilized design-based research methods to collaborate with participants in co-creating the workshop series. This study revealed that teachers believe UDL is important, however, they may have uneven knowledge of the framework. Phase One findings established that teachers were unfamiliar with the Down syndrome behavioural phenotype and did not value syndrome-specific information. Phase Two participants affirmed that pairing UDL checkpoints with behavioural phenotype is useful and found the collaborative nature of design-based research valuable. This study concluded that professional development highlighting both the complexity and utility of the UDL framework is necessary for comprehensive understanding and application. Learning about the Down syndrome behavioural phenotype offers concise information, serving as a starting point for inclusive teaching. Pairing UDL guidelines and checkpoints with syndrome-specific characteristics can optimize the benefits of both universal design and etiology-specific approaches.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".