1. It is time for a quantum leap in learning
Zoran Popović

2. My Story
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3. What problems are adults trying to solve?
Look at how we approached these challenges with Foldit
Game to tackle key problem in biochemistry: protein structure prediction
Proteins do all functions in all living organisms: understanding protein folding = secret to life
Problem is to determine how a particular protein will fold
Joint project with UW biochemistry Baker Lab
Players and groups compete to find high scoring protein structures
Real-time scoring based on scientifically accurate protein energy model
So high scores -> better folded

4. FoldIt experts: Adult learners without any subject-matter knowledge
Had no idea of the problem, and their potential
Emergence of skilled players with little biochemistry training
Many top players have little or no biochemistry background
In a survey of top players, 3/4 had no more than an undergraduate
beating biochemists who are playing the game

5. Add 5th paper?

7. Why can’t we do this for all education?

8. One size does not fit all
Anchored to rigid curriculum and instruction Ignores the unique needs of each learner Ignores learning environment differences

9. From narrow to infinite
Greater diversity needed
Curriculum component
20 to 100 times more content needed
Explain nodes
Setup the background
More is better?
Show individual paths

10. Personalized order Random content order Expert order
Explain highlight more

11. Why has EdTech failed so far?

12. We are doing the wrong things!
Intelligent tutors can’t replicate faculty Video lectures utilize one of the least effective classroom practices Non- and meta-cognitive aspects are not considered AI and machine learning lack precise interventions

13. Limited possibilities for AI
Explain nodes
Setup the background
More is better?
Show individual paths

14. AI on thought-process curriculum

15. What if…

16. Goal: Create real equity
Adult Learners:
These are school kids, but it’s even worse for adults 10 years out from a math class
Cannot “make more time” for adult learners
2 in the morning after kids are asleep

17. Goal: Create real equity
Adults
Years out of school
Goal: Create real equity
Adult Learners:
These are school kids, but it’s even worse for adults 10 years out from a math class
Cannot “make more time” for adult learners
2 in the morning after kids are asleep

18. Equity-focused disruption:
Focus on whole person engagement, productive struggle, mindset, problem solving Adjusts entire support structure real-time supports for faculty, peers Don’t waste a second real-time adjustment to immediate need Don’t teach what you don’t need custom curriculum for specific goals Learn by doing all support is just-in-time, as you need it, apprentice instead of lecture

19. Keys to successful EdTech
Follow learner thinking process
Generate critical curriculum on-the-fly
Create machine learning methods specifically to drive equity
Current “thinking process” work in available products is typically right/wrong or using a basic distractor. Generally useless.
Static curricula is ineffective, slow to change, so much more than the student needs in the moment
ML is often using a decades-old system that does not take advantage of the last 20 years of technology growth.

20. So, what is possible?

21. Mastery assessment
Solve 3 equations with behaviors that indicate fluency:
𝑎𝑥+𝑏= 𝑐+𝑑 𝑒
𝑎𝑏𝑥 𝑏𝑐 +𝑐+0=𝑑+𝑦𝑧
𝑏−𝑐 𝑥 =𝑐+𝑑 −𝑐

22. Algebra challenges Conducted on four populations
Norway
Minnesota
Washington State
Uruguay

23. 96% students reach full mastery
1.5 hours
96% students reach full mastery

24. Community college student experience
19 minutes on

25. Platform for the entire ecosystem
Infinite curriculum
Optimal pathway discovery
Student
Instructor
Optimize for both mastery and non-cognitive and meta-cognitive learner traits

26. Extrinsic motivation not a good fit: short-term effect

27. Enlearn engagement focus:
Student agency
Productive struggle
Meta-cognitive skills
Mindset change
Persistence

28. !
THE GOOD NEWS: mindsets can be taught (results from our published work on mindset change for over 20,000 students)
But the good news is that children’s mindsets can be changed through careful intervention. One study showed that directly teaching students that intelligence is malleable over a two month-period improved classroom motivation and grades compared to a control group. And even minimal interventions, such as praising children for their effort instead of their ability after a short problem-solving session, produced higher motivation and task persistence.
Mention Dweck

29. Instead of rewarding problems solved, reward productive struggle

30. Trying a new two move hypothesis “Fresh start”
“New idea”
Trying a new two move hypothesis
“Fresh start”
Clearing the board to start again
“Math effort”
Solving a math sub-problem
“Working hard”
Making ten distinct new moves
OBVIOUSLY THIS IS A TEST SCREEN FROM K-12, but the research has proven to be the same on adult learners.
These four metrics capture behaviors associated with effort and strategy use in Refraction. But during the my iterative design process, I discovered that if I rewarded these behaviors every time, students quickly learned how to game the system by repeating those behaviors over and over again.

31. More struggling students persist in the growth mindset version (increase of over 30%)
(χ2 =12.16, p<0.005, V=0.11)
Experimental
Control
125
100 75 50 25
Number of Players
This graph shows the number of players in each condition and each performance level who played for thirty minutes. The total number of children in the growth mindset condition who made it this far is greater, as we would expect given the time played result. However this graph shows that the number of struggling children who persist in the growth mindset condition is much larger than in the control condition. Also, it’s interesting to see that more advanced students persist in the control condition. This shows that rewarding effort and hard work encourages students who are struggling, while reward performance encourages students who are doing well already.
Advanced
Average
Struggling

32. Call to action
Focus on solutions that can reasonably scale Don’t expect a small tweak to make a big difference Complex systems do not get better by people thinking harder; they get better from rapid iteration Don’t give up on technology Demand that it specializes for your specific needs Demand big impact

33. Thank you!