Focusing on the learning goal supports educators to identify and reduce construct-irrelevant barriers so learners can access and engage with the construct-relevant learning goals. In other words, UDL helps educators keep desirable challenges in a learning experience and remove unnecessary barriers.
Very Important! And I agree 100%.
Emphasize identity as part of variability. Previous iterations have emphasized the remarkable variability among learners in terms of how they engage with learning (Multiple Means of Engagement, the “why” of learning), how they perceive information (Multiple Means of Representation, the “what” of learning), and how they act on and express what they know (Multiple Means of Action and Expression, the “how” of learning).
Very important to emphasize and to keep in mind.
Emphasize the value of interdependence and collective learning.
A very good suggestion. The value of interdependence in learning is very important.
Help students activate their cultural schema to access challenging content. Invite them to share where they come from, not just with you but also with each other. Value and affirm all forms of difference.
Giving students the extra support and giving them the little push to expand more is ideal to have them share a little more.
Once a week, have students meet in groups to share something they struggled with and what they learned in the process.
This would be a good idea and beneficial for the students as well.
No standardized test will provide you with quality data on these questions. Use proximity and lean-in assessment to diagnose students’ learning needs. Carry a clipboard with you while students are working, and take careful notes on what you observe.
I agree! Using a Lean-in assessment instead of standardized testing would be more efficient.
Many citations in this guide are fictitious,” meaning they are made-up examples used only to demonstrate proper formatting. Yet someone (or some AI chatbot) copied the fake example directly into the Education Accord report as if it were a real source.
Wow! I had no idea and this is very good to know.
AI language models like the kind that power ChatGPT, Gemini, and Claude excel at producing exactly this kind of believable fiction because they first and foremost produce plausible outputs, not accurate ones.
Good comparison and interesting to know.
When those patterns don’t align well with reality, the result is confident-sounding misinformation. Even AI models that can search the web for real sources can potentially fabricate citations, choose the wrong ones, or mischaracterize them.
I do agree, we all make mistakes.