What we covered:

– A recent report from the University of California San Diego revealed that 1 in 12 incoming freshmen were not proficient in middle school math – basically, anything above arithmetic with fractions. Their existing remedial math course was too advanced for these students, so they had to design even lower remedial remedial math courses. Even crazier, over a quarter of these students had a perfect 4.0 GPA in their high school math courses.

– It’s not just UCSD. This is everywhere. A similar thing happened at Harvard, too, having to add remedial support to their entry-level calculus courses. It’s like that movie Olympus Has Fallen, except this time it’s Harvard. It’s a catastrophe.

– How did things get this bad? Teachers and administrators face relentless pressure to inflate grades, and during the pandemic many universities went test-optional, removing the only signal that reliably correlated with actual math readiness. That decision simultaneously elevated high school grades to the sole gatekeeping metric, intensifying incentives to inflate them.

– This has all coincided with the advent of LLMs, which make it increasingly easy for students to cheat. The result was predictable: grades became untethered from real competence, and multiple cohorts of students entered college without ever having to demonstrate foundational math skills.

– Teachers have to play both good cop and bad cop, and there is no avoiding the latter. If you refuse to play bad cop at all, you eventually end up playing it constantly. The best teachers are strict from the start and ease up later, once students understand that hard, honest work is non-negotiable.

Outline:

00:00:00 - Introduction

00:02:11 - Freshmen math collapse: 1 in 12 UCSD freshmen don't know middle school math

00:06:45 - Remedial remedial math: UCSD created remediation for remedial math

00:08:40 - Inflated grades: 25% of remedial-remedial students had perfect GPA in HS math

00:10:06 - Test-optional admissions removed the last objective metric

00:12:13 - Pandemic inflation: GPAs skyrocketed

00:14:37 - Removing tests pressures teachers to inflate grades

00:16:52 - Grade-grubbing: endless negotiating, complaining, accusations

00:19:01 - Then vs. now: parents, tests, accountability

00:27:38 - Crisis opportunism: “Never let an emergency go to waste”

00:29:33 - No tests = no knowledge requirements

00:33:28 - Elite collapse: Harvard has the same problem

00:36:31 - No enforcement means no standards

00:37:40 - LLM cheating is trivially easy

00:38:25 - Catching a cheater and turning him around

00:48:46 - Cheating is like taking mob money. Now you’re in, you’re never out.

00:50:41 - Assessments must be done in person

00:55:06 - LLM cheating is often obvious yet hard to prove

00:57:17 - How to prevent cheating on long papers

00:58:28 - Start hardcore, then lighten up gradually

01:01:37 - Good teachers play bad cop when needed

Follow on X:

Math Academy - https://x.com/_MathAcademy_

Justin Skycak - https://x.com/justinskycak

Jason Roberts - https://x.com/exojason

What we covered:

– Most kids are not intrinsically motivated to do the hard things: practice their soccer drills, do their math homework, eat their broccoli. Getting them to do the hard things often requires gamification and/or incentives.

– A little gamification goes a long way. Jason gamified drills for his kids’ soccer team to get the most out of each practice (e.g., “zombie attack”), and it was unreasonably effective. XP and leaderboards on Math Academy are also unreasonably effective.

– A good incentive can change kids' behavior overnight. The incentive doesn’t need to be big; it just needs to be something the kid really cares about. Find the thing the kid would rather be doing, and use it to motivate them to do what they’re supposed to be doing. They won’t need the incentive forever; as the kid gets used to the feeling of a new behavior, it gradually turns into a habit that they can maintain on their own.

– Even when you’re doing what you love, there will be grindy phases. But kids typically don’t understand this. They might get interested in a talent domain and want to become good enough to build a life around it, while simultaneously resisting doing the hard work to make that happen (i.e., stage 2 in Bloom’s talent development process). It’s often up to parents, who can see the long game, to push their kids through the difficult parts in paths that they find rewarding.

– For instance, the most mathematically gifted student Justin ever worked with, who was drawn into math by his own intrinsic interest, still needed to be pushed to learn calculus. Now he’s having the time of his life working on physics-y, calculus-heavy research-level math problems in high school. Even after finding something he loves and is good at, he still needed to be pushed to do the hard work to unlock more of it.

Timestamps:

00:00:00 - Most kids are not intrinsically motivated to do hard things – homework, drills, practice. They usually need incentives to get through.

00:08:16 - A little gamification goes a long way. Jason gamified drills for his kids’ soccer team to get the most out of each practice (e.g., “zombie attack”).

00:14:05 - A good incentive can change behavior overnight. It doesn’t need to be big, just something the kid really cares about, and they won’t need it forever. It’s about building a habit until they can maintain it on their own.

00:41:17 - The stress of high school

00:54:16 - The most mathematically gifted student Justin ever worked with needed to be pushed to learn calculus, and now he's having the time of his life working on calculus-heavy research-level math problems.

01:11:54 - Even when you’re doing what you love, there will be grindy phases. It’s important for parents to help kids push through those grindy phases so that they can unlock more of what they love.

What we covered:

– Any successful endeavor requires a great team: capable people, who like and trust each other, and have complementary skillsets and ways of thinking. Some modes of thinking cannot be performed at the same time within a single brain.

– Accountability requires control. You can’t hold someone responsible for outcomes unless you also give them control over the system that produces those outcomes (though you can set reasonable operational boundaries).

– Solve today’s problems today. Smart people can invent endless hypotheticals and build giant solutions to fake problems. Not only does this waste time, but it also burdens the system with complexity that becomes a future straitjacket. Everything you build must be carried forward, so focus on what’s present in front of you, not on imagined futures five steps away.

– In a scaling system, the sheer volume of interactions will expose a long tail of bizarre scenarios, almost like rare diseases you’d never anticipate. Users will often try to repurpose software beyond its design, like hauling a trailer with a motorcycle.

Timestamps:

00:00 - Introduction

03:48 - The importance of finding your complements

24:07 - The origin story of Math Academy's content team

43:36 - No meta-work; just solve the problems in front of you

54:26 - Jason time vs real time (real time is longer)

59:00 - The long tail of rare edge cases and unexpected user behavior

What we covered:

– Building a knowledge graph is like city planning & road construction. Too many prerequisites leading into a single topic creates a cognitive traffic jam.

– Elegantly rewiring a live knowledge graph: the evolution of our tooling and automatic validations. How to avoid staging servers & migrations and NOT have it blow up in your face.

– UI work takes time and adds complexity, so we spend it on the customer. Internal tools are almost entirely command-line; clickable buttons are for customers.

– Justin's transition from research coding to real-time systems. He started with mathy, notebook-driven quant code and had to learn production engineering the hard way. Once he did, it was a massive level-up.

– Alex's plan for dealing with "content papercuts" - small issues that pile up. Inspired by Amazon’s “papercuts team.”

– Our upcoming differential equations course, the last course in the core undergrad engineering math sequence.

Timestamps:

00:00:00 - Building a production-grade knowledge graph is like city planning and road construction

00:07:26 - Elegantly rewiring a live knowledge graph: the evolution of our tooling and automatic validations

00:24:47 - Justin's transition from research coding to real-time systems

00:44:51 - Alex's plan for dealing with "content papercuts" - small issues that pile up

00:58:02 - Our upcoming differential equations course

00:00 - Intro: "problem solving" is what you call it when you don't really know what it is (i.e. you haven't explicitly enumerated the skills)

04:11 - How to approach research problems: Alex's PhD journey, top-down familiarity vs bottom-up mastery

20:28 - If you have natural talent, don't use it as a crutch. Don't turn your blessing into a curse.

29:06 - SAT prep, iteration 1: Realizing that the standard school curriculum leaves a massive “missing middle” unaddressed

33:45 - SAT prep, iteration 2: Covering the "missing middle" problems

53:38 - SAT prep, iteration 3: Building the "missing middle" knowledge graph

1:08:11 - Watching the manifold hypothesis play out in SAT prep

1:16:42 - The unreasonable effectiveness of the knowledge graph

00:00:00 - Intro: Willing Things Into Existence

00:11:43 - How Jason & Sandy Willed Math Academy Into Existence

00:36:45 - Fighting The Gravity of Mediocrity

01:02:29 - Case Studies in Educational Dysfunction

01:21:53 - The Birth of Justin’s Self-Study Madness

01:50:48 - Self-Studying on the Sly During School

02:02:41 - The Highs & Lows of High School Research

02:22:38 - Outro: Paving the Path with Math Academy

0:00 - What Would a Tutor Do, If Their Life Depended On It? (Part 1)

5:47 - Find Your North Star: Why Justin Quit His Data Science Job to do Math Tutoring Full Time

11:23 - Getting "Inside the Trade"

19:31 - What Would a Tutor Do, If Their Life Depended On It? (Part 2)

27:28 - Efficient Learning Techniques are Obvious if You Think About Athletics

33:45 - Enjoyment is a Second-Order Optimization

39:50 - We Need to Stay Hardcore, But Become Less Harsh

51:14 - Math Academy is Like "Yuri's Gym"

59:06 - Vision for the Future of Math Academy

1:14:23 - Goal Setting/Advising and Communicating Progress

1:24:58 - If All You Show Up With is AP Calculus, You're Probably Outgunned

1:51:08 - The Meta-Skills that Kids Need to Work Effectively on Math Academy

2:08:54 - How to Help Students Maintain Successful Learning Habits While Working Independently

2:32:29 - Overhelping: A Common Failure Mode of Well-Intentioned Parents/Tutors

0:00 - Introduction

4:00 - Applying the MA Way to X Growth

7:40 - Status of the ML Course and its Kick-Ass Coding Projects (Part 1)

25:50 - Jason's Near-Infinite List of Important Things

34:20 - The ML Course Has Been a Massive Undertaking

42:10 - Breadth-First Development

44:30 - Status of the ML Course and its Kick-Ass Coding Projects (Part 2)

50:15 - Why Math Academy Needs To Do a CS Course

56:45 - The Never-Ending Stream of Confusion

1:00:30 - The Story of Eurisko, the Most Advanced Math/CS Track in the USA

1:24:20 - Intuition Through Repetition: Machine Learning Edition

1:29:40 - The Importance of Spaced Review

1:43:30 - Upcoming Course Roadmap

1:47:40 - Spaced Repetition 2.0: Accounting For and Discouraging Reference Reliance

1:54:45 - Overhelping: A Pathology of the Over-Involved Parent/Tutor

1:59:21 - Yes, You Need to be Automatic on Math Facts (and Yes, Rapid-Fire Training is Coming)

2:04:55 - What Happens When Students Don't Know Their Math Facts

2:05:50 - The Horror of Attempting to Teach a Class When Students Have Multi-Year Deficits in Fundamental Skills

2:11:30 - Integrating Coding Into the Math Curriculum

2:18:00 - Combining Math and Coding is the Closest Thing to a Real-Life Superpower

2:18:55 - Creating a Full Math Degree and Getting Full College Credit

2:22:15 - The Power of Pre-Learning: The Greatest Educational Life Hack