UDL Implementation in Higher Education: Drawing lessons from the COVID online pivot and reconnecting with inclusive design in the face-to-face classroom
Bibliographic record
Abstract
After two decades of advocacy across North American campuses, it is fair to assert that Universal Design for Learning (UDL) is finally having an impact on the inclusion of students with disabilities across campuses (Schreffler et al., 2019). It is helping shift instructors and departments away from medical model approaches to students with disabilities (Edwards et al., 2022), and facilitating the adoption of the social model of disability in classroom practices (Fovet, 2014). In 2020, however, the COVID-19 pandemic forced campus closures and an overnight shift to online instruction and assessment across the world (Hodges et al, 2020). Many have argued that this pivot has helped increase awareness of accessibility and has developed inclusive design as a mindset among instructors (Dhawan, 2020). Equally numerous are researchers and practitioners who feel that the pandemic has weakened institutions’ commitment to inclusion, made accessible learning more difficult to achieve, and generally hindered the development of UDL in higher education (Napierala et al., 2022). This dichotomy in perspectives is pervasive and encountered in most jurisdictions; it demonstrates the need for higher educators to ‘reconnect’ despite these lived experiences and to journey collectively and collaboratively towards more inclusive practices, in this period of healing. This interactive session will lead the audience in assessing to what extent each of these assertions might be true, and how campuses can draw important lessons from these experiences, in relation to UDL implementation – particularly in Technology Enhanced Environments (TELs). It will examine how researchers and practitioners must draw from lessons learnt in online teaching and learning in these two disruptive years to ‘reconnect’ with the inclusive design mindset et advance UDL implementation as they return to the fade to face classroom. It will demonstrate how sometimes difficult and rushed reflections around inclusive design in TELs that occurred during the global health crisis, now have the potential to radically overhaul previous attitudes and assumptions, and to erode initial resistance to inclusive design as a mindset. The presentation draws from multiple interactive workshops which have been offered to UDL advocates and faculty throughout the pandemic. It presents the analysis of phenomenological data gathered throughout these professional development sessions.
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 | 0.034 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.015 | 0.022 |
| Scholarly communication | 0.019 | 0.020 |
| Open science | 0.005 | 0.021 |
| Research integrity | 0.007 | 0.014 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".