Bookmarked Moving beyond ‘solving’ problems as meaningful learning- a conference #ShrugCon by dave dave (

A conference about uncertainty which might also be about the left-overs after problem-solving.

Dave Cormier describes how, according to Herbert Simon, there are well-structured problems and the rest that is left over. Cormier explians why he prefers to focus on the “left overs”.

A well-structured problem almost never happens to me in real life. At work, as a parent, as a partner, as a citizen I am almost never in a position where I’m given a clear question that isn’t messy in some way, a process that I can follow, and a way for someone to say ‘yeah, you did that exactly right’. And when I am, I can mostly just use a GenAI tool to get there.

The things that are meaningful, to me, are about real life. They aren’t about chess, they aren’t about puzzles, they are about how each of us faces the uncertainty around us. With all these GenAI discussions swirling around I’m even more interested in how we learn when things are uncertain.

Moving beyond ‘solving’ problems as meaningful learning- a conference #ShrugCon by Dave Cormier

For me, this touches on Dan Meyer’s comments about ChatGPT-4o and mathematics:

This looks like success to many. To me it looks like someone has successfully diced an onion without understanding why we’re hosting the dinner party, what we hope our guests experience, or how we’re going to structure the evening.

We can focus students on larger ideas by asking other questions.

What is the question asking you to do?

What do you know about that?

What is special about this triangle?

What do you know about sine?

Source: ChatGPT-4o Will Be Great for Certain Math, Certain Thinking, and Certain Kids by Dan Meyer

Bookmarked Five Differences Between Human and AI Tutors (

This hope for chatbots takes a serious challenge—meeting the vast and varied needs of students—and trivializes it.

The positive framing for this article is that I have just described a product roadmap for AI chatbot tutors, one that they are moving ceaselessly along with every new language model release.

The negative framing is that we are asking a tool that is quite neat to do something that is far beyond its capabilities. by Dan Meyer

Reflecting upon recent experiences tutoring and being tutored, Dan Meyer provides five reasons why AI tutors will not replace human tutors:

– Human tutors seek context.
– Human tutors use multimedia.
– Human tutors create relationships.
– Human tutors are pushy.
– Human tutors know their limits.

Bookmarked (

From partisans of Inquiry-Based Learning™, I can take ideas for inviting student knowledge. I can take activity designs that draw out of students what they already know, activity designs that activate inert knowledge and yield mental resources a teacher can use in their direct instruction.

From proponents of Direct Instruction™, I an take ideas for developing student knowledge. I can take designs for teacher instruction that respect the cognitive architecture of the brain, principles for using multimedia in learning, and the ways all of the above can help students productively re-organize their existing ideas.

See? That was easy! You can change your life right now by starting with broad, sturdy premises about learning that cut across these branded, self-limiting ideas.

Source: How to Not Waste Your Only Life Debating Direct Instruction and Inquiry-Based Learning by Dan Meyer

Dan Meyer pushes back on the debate between Direct Instruction™ or Inquiry Based Learning™ instead calling for a middle ground where you borrow the best of both worlds. This reminds me of my idea of a ‘pedagogical cocktail‘. I think that the real challenge is actually knowing what you are drinking.

Replied to Are Great Teachers Born or Made? A cheat code for education (

I am convinced that a huge amount of the enthusiasm for AI in education (and for teaching machines historically) is simply the wish for a cheat code, a wish to press ↑↑↓↓←→←→BA, enter god mode, and escape our current condition where it’s hard to understand how to select, train, and support the people most essential to the education of our children. I’m suggesting that if you’re serious about this work, you can’t cheat code your way around teachers. If your work doesn’t account for teachers—the way they work, the way they move through a class, the tools they use, the way they think about their students, their aspirations for their work, the outcomes for which they’re accountable, the vastness of their experiences prior to teaching—you will make a meaningful impact on student learning only by accident. One possibility is that great teachers are born but that good teachers can be made.

Source: Are Great Teachers Born or Made? by Dan Meyer

Dan, I really like your point about artificial intelligence and a dream of a ‘cheat code’. This feels like an extension of ‘Uberification of education‘. I am also reminded of my discussions of greatness over building capacity.

Bookmarked Teaching After Twitter (

I am interested here as someone who realizes how much a) community, b) professional learning, and especially c) knowledge production arose from the particular context (including technological, political, personal, epidemiological, generational factors, etc) of 2005 to 2015. And I’m wondering where teachers–especially new teachers–will get that next.

With the changes to Twitter, Dan Meyer wonders where people might go to in order to engage with community now? He suggests that platforms like TikTok do not allow for the same level of engagement. This has me thinking about Dron and Anderson’s Teaching Crowds and Ian Guest’s research into Twitter. For Miguel Guhlin, the answer is Mastodon. He has published a number of posts to help with the transition.
Bookmarked The only question you need to ask about education technology. by Dan Meyer (Mathworlds)

My own rubric for evaluating edtech is very simple. One question tells me most of what I need to know.

What happens to wrong answers?

Dan Meyer discusses his simple rubric for evaluating edtech, “What happens to wrong answers?” He explains that “every wrong answer is a resource and we shouldn’t waste it.” The challenge is to value the student’s answer.

Meyer ends his post with a collection of others useful resources associated with reviewing technology, including a framework for thinking about technology centered in equity and Robert Talbert’s post re-considering points-based scoring.

Bookmarked Why Wordle Works, According to Desmos Lesson Developers by Dan Meyer (Mathworlds)

If you’re someone who designs learning experiences, I hope you’ll take Wordle as a challenge.

  • Can you create a wealth of learning opportunities with only a simple prompt?
  • Can you design the activity and support so that everyone learns as much from failure as success?
  • Can you offer feedback that goes beyond “right” and “wrong,” that helps learners identify everything right about their wrong answers?
  • Can you make room for multiple paths to correctness?
  • Can you offer learners a representation of their learning they can share with other people?
Approaching Wordle from the perspective of learning and teaching, Dan Meyer summarises five ingredients that have helped make it work so well.

  • Failure is expected.
  • Effective feedback.
  • Different routes to the same answer.
  • Your learning results in a product you can share.
  • It’s social

For a different perspective, Daniel Victor provides a profile of Josh Wardle and the meteoric rise of the once-a-day game. While as an alternative, sajadmh has created a version of Wordle in Google Sheets.

Bookmarked The #1 Most Requested Desmos Feature Right Now, and What We Could Do Instead by Dan Meyer (

We also want students to know that there are lots of interesting ways to be right in math class, and that wrong answers are useful for learning. That’s why we ask students to estimate, argue, notice, and wonder. It’s why we have built so many tools for facilitating conversations in math class. It’s also why we don’t generally give students immediate feedback that their answers are “right” or “wrong.” That kind of feedback often ends productive conversations before they begin.

Dan Meyer responds to the request that is often made for automated feedback, suggesting that such feedback is problematic.
Bookmarked “Real-World” Math Is Everywhere or It’s Nowhere by By Dan Meyer (dy/dan)

Amare is looking at these 16 parabolas. Her partner Geoff has chosen one and she has to figure out which one by asking yes-or-no questions. There are lots of details here. She’s trying to foc…

Dan Meyer on differentiating between ‘real’ models versus ‘non-real’ models in Mathematics. The problem with this is that from a process point of view it is all real learning.
Liked That Isn’t a Mistake by Dan Meyer (dy/dan)

It’s a bad mirror, so I call it a mistake. “Mistakes grow your brain,” I say. “We expect them, respect them, inspect them, and correct them here,” I say. And if we have to label student ideas “mistakes,” maybe those are good messages to attach to that label.

But the vast majority of the work we label “mistakes” is students doing exactly what they meant to do.

We just don’t understand what they meant to do.

Bookmarked dy/dan by By Dan Meyer (dy/dan)

Hi. I’m Dan Meyer. I taught high school math to students who didn’t like high school math. I have advocated for better math instruction here and on CNN, Good Morning America, Everyday With Rachel Ray, and I earned my doctorate from Stanford University in math education and I’m currently the Chief Academic Officer at Desmos where I explore the future of math, technology, and learning. I have worked with teachers internationally and in all fifty United States. I was named one of Tech & Learning’s 30 Leaders of the Future. I live in Oakland, CA.

I remember being introduced to Meyer’s work a few years ago. He takes problem based learning in Mathematics to a new level.