Mike Goldstein, the founder of Match Education and 1Up Career Coaching, is one of my favorite education thinkers. Over the years, he’s often been decades ahead of the pack when it comes to rethinking tutoring, teacher pay, professional development, school models, and more. He’s occasionally shared glimpses of his thinking here at RHSU. Well, he wrote recently, after his nonprofit 1Up Career Coaching was featured in best-selling author Dan Heath’s new book RESET: How to Change What’s Not Working. I was intrigued by Goldstein’s take, per usual. Here’s what he had to say:
—Rick

Dear Rick,

Last fall, after the election, you wrote that there’s an opportunity for the education community to engage in some much-needed reflection and to rethink some of the areas where we’re stuck. When I read that, I was reminded of Dan Heath’s new book RESET: How to Change What’s Not Working. You may know Dan, the best-selling author of Made to Stick and Switch.

In RESET, he digs into broken systems—hospitals where packages take three days to show up, animal shelters that can’t get people to adopt cats, fast food restaurants where drive-thrus take forever—and how to fix them.

One example of a failed system Dan provides is K-16 education: He explores the problem where many first-generation college graduates struggle to find good entry-level jobs. They apply to dozens of openings and get no replies, let alone interviews. Dan found his way to my friend Geordie and me. We were curious about what was causing this and where the conventional wisdom might be wrong. When Geordie and I interviewed these “undermatched” college grads, they expressed exasperation and sometimes shame. “What’s wrong with me?” they’d ask.

In Dan’s parlance, our next step was to “map the system.” Yes, their high schools never delved deeply into career exploration nor realistic entry-level job expectations; yes, they’d sort of haphazardly chosen majors; yes, they hadn’t really built the social capital in the form of “connections” that would genuinely help them chase down jobs after graduation.

But the key leverage point was in the last part of their K-16 experience. Despite job-search support offered by university career centers, recent grads continued to struggle—a reality that makes sense, given that career-center programs empirically have a bad track record. In Dan’s “map the system” parlance, we thought, that’s where the gold was buried—the opportunity for quick, productive change.

So, Rick, we took a page from your book Cage-Busting Leadership. Instead of creating one more career-coaching program, we decided to invert things. Usually, education interventions are designed to have a fixed dosage of some input and then see what happens. Even when I conceived of “high-dosage math tutoring” at Match, all that meant was the dosage of inputs was bigger and of better quality! But we still weren’t steering toward a concrete outcome.

To help struggling post-grads, we decided to do things differently: We held the result constant and varied the dosage. Geordie and I first tried this approach with a small sample. We decided we would do whatever it took, for as long as it took, to get a concrete result: Find them a new job that would pay 20% more and raise alumni satisfaction from 4 out of 10—the average baseline job satisfaction among our 1Up clients in their current “undermatched” job—to 8 out of 10.

Instead of giving generic advice for each step of the job search, we’d sit there patiently alongside students and recent graduates and just do it: search the internet to seek out jobs they might like, read job descriptions together, bypass the college’s inefficient Handshake click-to-apply tool—and instead find actual humans who they could email directly—and draft and send applications on the spot. When interviews came along, we’d drop everything to help them prepare.

We whipped up a little nonprofit, 1Up Career Coaching, to provide this service. We soon realized we’d need to reject conventional practices for our clients to succeed. For one thing, with permission, we were honest to the point of bluntness—there was no other way to do the work. For another, there was no generic “prep” that helped. If someone had an interview coming up, we worked with them 24 hours beforehand for that particular interview, for over an hour, creating key lines and rehearsing them over and over until they flowed. For in-person interviews, we plotted the Uber drop-off point and target time. For an interview on Zoom, we adjusted lighting, camera angle, background, and sound level.

It worked! Our first 30 people found new jobs. Then another 50.

Now, we’ve shifted our focus from charter school alumni to a different cohort of low-social-capital college grads: middle-aged moms who attend one of the largest online colleges in the U.S. The program is working for them, too.

Rick, you’ve written, and I agree, that policy can be a bad tool because it can require things to be done, but it can’t require that things be done well. Your insight explains not just why many school-based interventions don’t produce meaningful outcomes but also why the few that do in small trials don’t scale well. The people running these interventions never know when the “job is done.”

Most interventions just change an arbitrary “statistically significant improvement”—a kid at the 50th percentile makes it to the 53rd, for example. That allows the program and the evaluator to claim success. But it’s not an easy-to-grasp target. By contrast, if you are building a table, tutoring a kid until he can pass a certain test, or counseling a 22-year-old until they have actually landed a new job—that’s a more concrete targeted outcome.

What if interventions first achieved a concrete, clear gain like “whatever it takes to read reasonably well” or “whatever it takes to stop being clinically depressed,” no matter the cost in time or resources? Then, only after we know what it takes to achieve a goal—both the dosage of an intervention and the human skills needed to deliver that well—do we examine the resource constraints.

I know what you’re thinking: How can schools afford this?

First, sometimes the money is already there. Take Boston, where schools spend over $30,000 annually for each student. Over 13 years from K-12, that’s $390,000 per child. What if parents controlled that money? They could spend $20,000 per year on basic schooling and save $10,000 per year for bursts of high-dosage help when needed. If their 3rd grader struggles to read, or their 8th grader was spiraling into depression, or their 10th grader seemingly had a shot to be really good at tennis, they’d do what wealthy families do: Hire intensive help until the child is curling up with Harry Potter, stabilized, or hitting 105 mph serves. Wealthy families don’t pay for help that only moves their child up .08 of a standard deviation and results in their child still being a struggling reader. They buy the dosage and quality needed to get the job done.

Second, artificial intelligence can lower costs in the long term, but it must replicate something successful. Right now, we’re using AI to replace weak interventions, like replacing low-quality human tutoring and counseling with even weaker ones. That’s like trying to fix a wobbly table by removing another leg. Instead, we should use AI to enhance proven systems. Once we’ve anchored to results, we can explore how AI might improve efficiency or scalability.

Dan Heath’s RESET is about programs that approach challenges from a new direction. In the education sector, the endless stream of half measures and weak interventions with bad incentives is not leading to the desired outcomes. We need a new direction.

It’s time to anchor R&D to clear, meaningful outcomes and do whatever it takes to achieve them. That means taking small steps—and often failing and recalibrating—to achieve durable wins. Whether it’s a college grad landing a great job or an 8th grader mastering fractions, the principle is the same: We shouldn’t stop halfway or arbitrarily dole out “help” that doesn’t produce a concrete result. We keep going until the job is done; then, we come up for air, look around, and see what it takes. Only then is it time to have the hard conversations about resource constraints.