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This checklist positions the UDL checkpoints across a progression pathway from emerging UDL practice, to proficient UDL practice and finally to expert UDL practice.
The checklist is framed by the three core UDL principles and includes the various guidelines and checkpoints outlined by CAST. It takes the form of a progression style rubric, adapted from a rubric developed by Melissa Toland. Both checklist styles are available below in Google doc format. Please feel free to adapt as required to your own context, creative commons licensing applies.
Creative Commons Attribution (CC BY)
This work is licensed under a CC BY license, allowing sharing and adaptation with proper attribution.
https://creativecommons.org/licenses/by/4.0/If you have modified or adopted this resource, share your version here. Tracking adaptations helps us measure impact and connects others with useful updates.
This report draws on Patricia Gibson’s research around critical approaches to Artificial Intelligence (AI) in Education and the increasing automation of Educational Technologies (EdTech).
This resource was created to share the learning from teaching adults with intellectual disabilities (ID) in IADT college as part of a national suite of pilot projects.
Comparing student and educator perceptions of generative artificial intelligence for teaching, learning and assessment: results from two institutional surveys
This case study outlines a first-year intervention at SETU Waterford using a timetabled weekly session to tackle common causes of academic failure such as time management, assessment planning. and study skills. It is intended for programme teams seeking practical, low-resource approaches to improving student progression and retention.
Following this link opens a new browser tab and sends you to an external website outside of the National Resource Hub
URL: https://www.dcu.ie/teu/universal-design-learning-toolkit-july-2021
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