How to Make School, Tech, & Human Learning Better Now With Justin Reich

Alexander McCaig (00:08):

Hey, Justin, thank you for joining me today on TARTLE Cast. You recently wrote a book. Well, you've been up at MIT, called Failure to Disrupt. And what I find interesting about this, is that you geographically, where you are, you're essentially the technological research hub of a good majority of our breakthroughs outside of the ones that happen on the west coast, in higher education. And the intersection of how we educate people in the advancement of technology in that stance.

Alexander McCaig (00:40):

You've brought both of those points together, to see where things have essentially failed since their start in the sixties with Plato. Up until where we are now with these massive online courses. And how we can sort of refine that, so it works better with human biology and our own development. So that when we do talk about learning in the future, and advancing how we learn, making it more cost effective, making it more personalized. You have a lot of good points that drive us towards one of those reasonable scenarios. So thank you very much for writing that book, Failure to Disrupt.

Justin Reich (01:13):

Sure. Happy to be here.

Alexander McCaig (01:15):

So I think to kick this off, everybody thinks that, and I guess I can use Carl Young here. He was adamantly against sort of applying statistical curves to people. And saying that when we put people in large groups, you can define things by numbers. But when it comes to learning, there's so much individuality, so much flavor and personality to that person, right? Especially at a psychological level. That if I try to apply something from a statistical measure onto them, and then assume that's going to work, the reduction of that identity does not actually translate into something that's really meaningful for that individual.

Alexander McCaig (01:56):

Now everybody's been hot to try on things like edX, Coursera. Even language learning, like you talked about with like Duolingo. But we've been trying to do this since the 1960s with a program called Plato. Right? And I'd love if you could kind of kick off the real origin of where this intersection of technology and automated learning started to occur. And then how that bridges to where we are now. And then we can develop further from there.

Justin Reich (02:25):

Yeah. I think people think of education technology as new. And certainly folks who are entrepreneurs, who are creating new education technology products and services, try to argue that they are profoundly, radically new. But they're not. For as long as we've had computers, since we had computer that were the size of your living room. Computer scientists and learning scientists have partnered to create computer programs that help teach human beings.

Justin Reich (02:59):

Even before we had computers, there were engineers who were trying to build mechanical systems that could automatically teach human beings. My colleague, Audrey Waters, who ran for a number of years, a really important blog called Hack Education, has just published a book with MIT Press called Teaching Machines. Where she traces the work of folks like Sidney Pressey and B.F. Skinner, who people are probably very familiar with from his work on behaviorism, and their efforts to build mechanical machines that teach people.

Justin Reich (03:33):

And the theory behind those mechanical machines, was something along the lines of each person learns at a slightly different pace and path than other people. There's no way for a single human teacher to effectively differentiate instruction for a class of 26 elementary students, or a lecture hall of 140 college undergraduates. So let's create machines, which can somehow optimize the individual learning trajectories of each student.

Justin Reich (04:12):

And there's certain parts of the idea which are historically compelling. There's certain parts of the idea for which I think there's good evidence behind and is still compelling. What has consistently failed to take hold in the 1930s with Sidney Pressey, in the 1960s with these first computer based systems, and [inaudible 00:04:32] Plato that you mentioned. Is folks are constantly hoping that these new machines, as they enter educational systems, will disrupt or transform those systems.

Justin Reich (04:45):

People have this imagination like, oh, well, if we could build machines, which can automatically teach people, then we won't need humans in the same way, we won't need classroom, we won't need... I mean, we'll be able to sort of completely erase the existing systems and generate new ones. And that has never happened. And it is very unlikely to happen say, in our lifetimes, right?

Justin Reich (05:07):

Because new technologies don't disrupt existing systems, new technologies are domesticated by existing systems. These really complex rich educational systems. They look at new technologies, and they go, okay, we'll use you, but you fit here in this particular place, for this particular population, for this particular purpose. They're domesticated and they aren't disruptive.

Alexander McCaig (05:30):

So help me understand this. Then if I'm taking something with sort of a statistical approach, like this. If I want to coin something as disruptive. Or I want to say, I want to disrupt education, which these entrepreneurs do, in some sense for EdTech. How is it that they define learning? Or how is it that you would define learning? So the way I learn, fundamentally how I defined it in my life through my own perspective, is completely different from many other people. My learning's very nuanced. So how is it that you could generally apply learning through a definition, that could be applied in broad strokes to many different people?

Justin Reich (06:11):

Yeah, I think that's a great question. There are ways of thinking about learning that emphasize an extraordinary, and I think really beautiful, consistency in human biology. A key feature of human brains for instance, is that we have pretty limited working memory. We can only hold a few things to think about at any given time, usually between three to five. And it doesn't matter if you're from Siberia, or Patagonia, or Nigeria, or from China. Once you're of adolescence, it doesn't matter if you're old or young, rich or poor, all those kinds of things. Actually, virtually all human beings have very similar cognitive architecture in some domains. Now, of course, we all also have beautifully, and a thing that's really beautiful of that, that's like a beautiful sameness. It is a beautiful, consistent part. It's something that is our shared heritage as human beings on this earth.

Justin Reich (07:14):

We're also all really, really different. We have different interests, we come from different cultural backgrounds, we have different background knowledge. And so I think the field of education is constantly wrestling with the duality that we find in lots of other parts of the human experience. Is that in some respects, we really are all the same. And in some respects, we're all wonderfully different. There are ways that we can leverage some of this sameness.

Justin Reich (07:43):

People who are really proficient at mathematics, know a lot about math. For instance, typically at a relatively young age, they develop a pretty effective mastery of math facts. The times tables, the addition tables, things like that. And with certain kinds of algorithms that have become standard. How to do long division, how to multiply and divide fractions, those kinds of things. There may be all kinds of beautiful, very different ways, that you take that mathematical knowledge and apply it in your life and your interests.

Justin Reich (08:16):

But there's actually a lot that's quite a bit the same and universal, about the kinds of facts that you need to be successful, as a human being who's trying to use math to do interesting things. So we can build software that helps you develop that fundamental mathematical learning. It will not work for all human beings, right? There are some folks who take a lot of motivation from learning from peers, and learning from mentors. Who, for a variety of reasons, really struggle with any form of online learning, distance learning, education technology tools.

Justin Reich (08:58):

But there are also folks who prefer learning online, or prefer learning from computers. Who like the independence, the self-direction. We're discovering from the pandemic, that they're all kinds of folks sort of classified with certain kinds of learning disabilities, where really, they just find it really anxiety provoking to be in this sort of loud social circumstances of classrooms. And they did a lot better when they were at home.

Justin Reich (09:27):

But there's a whole lot of people in the middle, for whom online learning is fine. It's one thing they do, but maybe not the only thing they can do. And as as a human civilization over the last 60 years, we've learned a lot about how you build cognitive tutors, intelligent tutors, that help people learn some stuff about some pretty important things in math. Now, one of the neat things about math is that it is a field in which there are well defined right and wrong answers. And in fact, computers are pretty good at detecting whether or not people can find those right or wrong answers.

Justin Reich (10:04):

So I could sit you or your child, if you have them, in front of a math app, an intelligent tutoring math app. And it would actually do a pretty good job of figuring out how much you know about some pretty important background facts and background procedures for math. Background facts and background procedures are not everything that's important in math. Some things that are important in math are visualizing features in an environment that are important to manipulate mathematically.

Alexander McCaig (10:34):

So hold on, are you saying that I'd have to... Just stop right there. You're saying there becomes a point when I understand a fundamental base of things, that allow me to then come into the world itself through my perspective, and then become interpretive. Where I take the fundamental base, and then I can interpret that concept, or percept, or whatever it might be of what I'm looking at, and apply it to math itself. So then it really becomes an experiential type of learning. Is there a step that is taken there? Is there a limitation?

Justin Reich (11:10):

I would discourage people from thinking about it as a really good math education is one in which you study basic facts for a long time. And then sometime way in the future, you're sort of permitted to do interesting things. What we should be doing at every stage is saying, okay, you learned how to do some addition in your kindergarten class, go out into the world and add interesting things. See what addition does to let you manipulate the world around you. And let's evaluate not just whether you can compute math facts correctly, but whether you can talk in meaningful way and reason for evidence about mathematics. What can you add one cat to a second cat? Can you add one cat to a dog? Is that one and one, or is that two of the things?

Justin Reich (11:55):

If you add one raindrop to another raindrop and they stick together, did you get two things or one things? Those are things that actually, mathematicians don't have really clear answers to, because they're sort of definitional problems. But they allow us to think about the ways that even really young people are reasoning about mathematics. So you learn some addition facts, and then you tell us about how you're reasoning about mathematics. And then you learn some multiplication facts, and you tell us how you're reasoning about mathematics. Then you learn how to factor polynomials. It's a sort of iterative process back and forth. The crucial thing for computer based learning, is that we have really good tools that can evaluate the quality of your computation. And we have very, very poor tools for evaluating your ability to reason from evidence.

Alexander McCaig (12:37):

Well, doesn't it feel like the largest drawback to anything? You can shove words down my throat all day long with Duolingo. And I can regurgitate maybe a word, or three words, that sit in a specific phrase. But the moment I step into conversation, my ability to reason within that conversation becomes very difficult. My ability to reason on, what is it that I need to use in the context of the people, or the environment that I'm actually in, right?

Alexander McCaig (13:03):

So when I intake knowledge, it's all wonderful,, but that knowledge doesn't really become something wise in its use unless I have the experience to apply with it. So I feel like when I look at these systems, there's sort of a sterile nature to it, with a natural limitation, that if it doesn't carry itself out into some sort of real world experience, then the formation, and remembrance, and retention of that information within the mind does not stick. From an emotional, or even a physical hormonal level for how you actually adjust physically to an environment.

Alexander McCaig (13:36):

So it's like, I go to training in the military, right? I can put an individual in a flight simulator all day long. It's efficient for us, we don't have to pay for fuel, the insurance is going to be less. But it's a completely different story when you actually move the jet upside down, and the g-force is changed, and you actually start to black out. So then you have this appropriation of the knowledge I've accumulated, but when you put me in the actual scenario of using that knowledge, I'm almost lacking at that point. So how is it that Edtech, or from what I've read in the book, when they fail to disrupt, how do we actually do disruption properly? How do we bridge into that level of experience? So it is personal so that we can really look at the reasoning itself, if we're all individualized.

Justin Reich (14:26):

What I try to argue in the book, is that disruption is the wrong goal, because we haven't successfully done that for decades, and the right goal. It would be nice if we knew how to build technologies that could do all the things that we wanted them to do. We would've built them by now. I mean, people have put millions of dollars, many hours of human time, years of human time. And we don't know how to do all the things that we want to do. So we're stuck with an educational system in which computers are good for some things. And we still need humans to do many of the other things. Where our computer technologies, or digital technologies, they get better, but they get better actually relatively slowly. It takes a lot of time, a lot of investment, a lot of testing, a lot of effort to make things improve.

Justin Reich (15:14):

I think you make a valid point, when we ask people to do complex performances in the real world, experiential education plays a really important role in having people develop mastery of those things. However, in my view, that shouldn't diminish the importance of a preparation which can feel more rote or more procedural, and being able to do those things.

Justin Reich (15:47):

We wouldn't have great musicians, if what musicians did all the time was just play pieces. Which is actually what we want them to do at the end, is to play really beautiful, orchestral performances. But musicians spend a lot of time playing scales. And the reason why they play scales, is when you develop mastery and fluency in specific pieces of a domain, you develop automaticity in those domains. And then when you get into more complex performances, you can basically sort of call on those well rehearsed materials more easily, while your attention shifts to things that are more important.

Justin Reich (16:27):

So we want to put people in flight simulators, to make as many of the experiences of fly a plane, have some kind of automaticity as possible. So that the first time you're upside down and experiencing g-forces, you can have your working memory attend to those novel experiences, while there's a whole bunch of things that you will have some automaticity around. The fact that we can't build a flight simulator that teaches you how to do every thing to fly, is not a condemnation of flight simulators, is not a failure of flight simulators. What the wise educator does, is recognize those limitations.

Justin Reich (17:03):

The problem that we've had, particularly in K12 and higher education, is a bunch of education technology evangelists coming and saying, we have a bunch of technologies that will do all of this. They're going to disrupt and transform educational systems, they're going to build these completely new, personalized pathways. And they're not. They're going to be useful for specific things, in specific context, for specific students. And we need to use them and continue to improve them in these particular domains. But we still need really great human educators to figure out all the things that computers are not doing well, and to build really rich learning experiences for people around those things.

Alexander McCaig (17:39):

Okay. So does the inverse of that model work at all? So for instance, who's a good example? Leonardo DaVinci. Leonardo didn't necessarily have a formal education, and there was no automated systems at his time. But you're talking about something very abstract, like art. Now, if the teacher he's brought to, puts him in a classroom with many other students at the same time. And recognizes, the teacher does, through the nuance of interpretation in the experience of that individual over the course of time. Says that this child has a gift for what it's doing.

Alexander McCaig (18:22):

Doesn't it seem like, if we don't take that approach first, by looking at each person as individuals, but going with the statistical foundations first of learning, says that we don't really look to the light or the things where people really do flourish in their own character. Upbringing, and nature within themselves, even biologically. And say that, why didn't you start with all this general stuff first? And then you try and filter that out, and figure out what you want to become or really want to go into and become an expert in. Rather than tend to the natural skill of the individual from a very interpretive and experiential view of the teacher coming in first, I guess, as the point of a guru or someone who can actually recognize that sort of skill. Whether it be in mathematics, right? Or painting, or writing, anything of that sort. How come no one's looked at that inverse model when it's worked so well for so many great people that truly have disrupted civilization so many times, by coming at it from the inverse approach.

Justin Reich (19:21):

Well, I certainly think there are educators who have thought about, how do we cultivate every individual to their own full potential? Usually those models involve sort of tutoring and apprenticeship. And they tend to be enormously expensive. There are 57 million school kids in the United States.

Alexander McCaig (19:41):

So it's cost? So then we take the statistical model of, let's not really look to the really shining parts of this individual human being. We're just going to throw them in a bucket, give them normal model because it's economically efficient for us to do so.

Justin Reich (20:02):

I think that's certainly part of it. Because doing anything with 57 million young people is going to be a challenging organizational thing. But here's another way of thinking about it. In my view, a really great school system would find things that individuals care about, are interested in, have some affinity for, and help them develop proficiencies in those own individual pathways. Even if a school system was outstanding at doing that, I don't think that's all it should do. I think part of what we should do, particularly in a democratic society, but in any kind of civil society really, is come to some consensus about things that we want everyone to know, everyone to have some facility with, regardless of their individual proclivities.

Justin Reich (20:54):

For instance, I used to work for an organization called Facing History in Ourselves. Which builds curriculum materials that helps folks learn about the Holocaust. I think every human being should learn about the Holocaust and the terrible depths of evil that exist within human beings. And I think we should not leave it to individuals to sort of see whether or not they happen to have an interest in the Holocaust, or have some affinity for it, or things like that. I think there are certain things which are really essentially important for everyone to learn, regardless of their proclivities, or orientation, or interest in it. The Holocaust is one of them, early language acquisition is another one, some parts of mathematics. I mean, a huge part of the debate that we have in school systems, is what those things that would be good for everyone should be. How a democracy works, what a citizen's role in it and stuff like that.

Justin Reich (21:47):

So in my view, a really excellent school system would say, there's something that we as a community have agreed that all of the people in our community, regardless of their background, of their interests, of their natural talents, should learn about. Because we think it's a really important part of being in our community. And then schools should also have some resources that are devoted to saying, well, what does this person really want? What are they good at? And what are they interested in? Sort of cultivating that to. I think there's no question that our school systems spend far too much time on the former rather than the latter. But I still think if we built the ideal school system, it would still include a whole bunch of the former.

Alexander McCaig (22:30):

So if I understand this, there's a debate between individuals, trying to reason which thing is most appropriate to then champion off to the people within the school system for them to learn, correct?

Justin Reich (22:42):

Sure.

Alexander McCaig (22:43):

So then why wouldn't we just teach people how to reason? Why wouldn't that just be the absolute fundamental thing? And then from that point, the individual then makes choice on the path of learning. And if they know how to reason they're going to do the best thing possible. Right?

Justin Reich (22:58):

So in the earliest 20th century, many educators believed that the best way to teach people how to reason was by teaching them Latin and Greek. And there was a psychologist, named Edward Thorndike, who developed a theory called transfer. And he argued that actually learning Latin and Greek, wasn't a good mechanism for universal reasoning, because there actually wasn't universal reasoning. That the way that you reason in Latin and Greek, at the time he would've said, is different than the way that you reason in the sciences, or reason in mathematics and things like that.

Justin Reich (23:40):

So there's two challenges with teaching everyone how to reason. One, is there is not a universal set of reasoning facilities. The way we reason in cooking, in computer science, in hair dressing, are unique and distinctive. Another thing about reasoning is that there's lots of good evidence to suggest that people who reason proficiently, have very deep factual knowledge in the domain in which they're reasoning.

Justin Reich (24:10):

A classic set of studies with this happened with chess. So people took a bunch of sort of famous chess positions, chess boards that would appear in the course of play, and showed them to novices and showed experts. You show them the board for a little while, and then you take the board away, and then you ask them to recreate the boards with pieces that are lying around. The chess experts are very proficient at this, the chess novices are not very proficient at this. So there's a set of factual knowledge that chess players have about these positions that they draw on.

Justin Reich (24:45):

They then, an interesting thought, the sort of next piece of this is you show random chess boards to chess experts and chess novices, and the chess experts are no better looking at the random chess boards than the novices are. They don't have some universal, sort of pattern recognition facility, or memorization facility, or something like that. What they've done to become a chess expert, is to systematically memorize a whole series of facts about chess. And that's how you perform effective chess reasoning. There's good reason to believe that, that's actually what really powerful reasoning looks like in most other domains. If you want to be really good at engineering reasoning, you need to have a whole bunch of facts in your head about engineering. If you want to be good at poetry reasoning, you need to have a whole bunch of facts in your head about poetry.

Alexander McCaig (25:39):

So if I think of that reasoning and the way you describe it, and I think maybe the divide of the thought is this. That's not so much reasoning about the external, telling people how to reason about the external world, but how a reason with themselves, right? Is that the function of critical thought? It's all well and good to look at a chessboard, something external for me. But how do I reason with my own internal self and my own internal thoughts, that would then lead me to other systems or ideas or things that I really want to follow through with.

Alexander McCaig (26:07):

I feel like that sort of teaching element, of reasoning internally for the individual, then becomes something that they can project to the outside world and start to reason, maybe it is within chess, or mathematics, or engineering. But I feel like there's so much looking on the outside with the disruption, and forcing things from the outside to the in, that it lacks sort of the balance of what goes on in the individual's mind in their own development. Where they can reason this is something I actually enjoy because I know myself, and what I want, and what I want to do. Or maybe I don't know what I want to do, but I can lead myself there, by figuring out what works for me and what doesn't. And so when I look at that sort of EdTech model, the way I look at it, I would love to see it in an inverse model, right? Where the thinking happens with the individual first, and then it becomes something external that we can work with. You see what I'm saying?

Justin Reich (27:00):

Probably partially. I mean, I think it sounds to me like you have some interest in having school systems sort of spend more time helping people be reflective about what their interests are and motivations are. And that seems like a worthwhile thing.

Alexander McCaig (27:17):

But it doesn't seem costly though, does it? I mean, can't you just ask a very simple... I mean, it doesn't cost me anything to ask the question, why? Or even to ask me that it to myself. So when I hear things within this model where it's boiled down into the economic sense, Justin. It doesn't really make much sense on how you apply that to actual human nature and their own understanding. I mean, it's great we can understand everything else, but if we look throughout our course of history, we have a hard time just understanding ourselves. So how we supposed to understand the other, you see what I'm saying?

Alexander McCaig (27:46):

For me, if I were to understand this, if you were to teach me about the Holocaust, right? And the travesties actually occurred, and the taking of those lives. Well, I would first have to understand within myself the value of human life. And then also reason with myself, what I determine are my best morals. But I feel like if I say, well, let's look at it in this way. If we take the statistical number of, there was this many millions of Jewish people that died, but we have 7 billion people in the world, percentage wise it's not really that much.

Alexander McCaig (28:14):

Now there's two different stories here when I put life in sort of the statistical sense, rather than the sense of the value for what it means to have a human life. So when in terms of teaching, I don't think the statistical route is typically the one that should happen first. And I do agree with a lot of points in your book. Where if that failure to disrupt, I think it still does require having that teacher. But I think the teacher needs to take that step towards the introspective part of helping people reason within themselves first, before they reason the outside world. Do you see where I'm coming from?

Justin Reich (28:43):

Yeah, I think it makes sense. I mean, an interesting part of the facing [inaudible 00:28:53] ourselves curriculum, is it actually does begin with sort of reflections of identity, including individual identity. And I think there's some sense that really effective math instruction starts from having people think about, what are their own mathematical intuitions? And how do they start by reasoning mathematically? I don't think I would characterize it as, you have to do all of the internal work before you can do any external work. Probably more that what really effective teachers do, is they are constantly making connections between people's interests, people's motivations, people's values and their thinking. And then the external world.

Justin Reich (29:38):

The other thing is that, people don't come into the world, all of them, intuitively with really rich ideas about how to reason for themselves about those things. People don't come into the world with morality. The way we develop morality is that we study moral systems. We study the one in our family first, and the religious traditions, and the cultural traditions we come from. And then our ability to engage in moral reasoning is enriched by studying other kinds of moral reasoning systems that are external.

Justin Reich (30:11):

But I do disagree with the idea, whatever you propose that you want human beings to get better at, it's expensive. It may not be expensive to ask people the question why. But to put young people in circumstances in which there are well trained mentors with the time and resources to help them develop a capacity of almost any capacity, is almost always an extraordinarily expensive prospect. I have my students at MIT, a lot of them are advocates for having more computer science curriculum, more computer science teaching in the state curriculum.

Justin Reich (30:49):

And we sort of do like a Fermi problem exercise of saying well, all right, if you wanted to have all the students have a well qualified teacher who taught them three years of additional coursework in computer science. And we have very few teachers who are equipped to do that now. What would that cost on an annual basis? And you come to realize pretty quickly that the educational system is complicated, and if you want these things driven by really well qualified professionals, it's expensive to build the capacity, and to hire the time of those well qualified professionals.

Alexander McCaig (31:32):

Interesting. And so then I guess that boils back to the nature of the book is, how is it that you can create something that's economically efficient, that can act as you a temporary placeholder for those experts that otherwise can't be in developing countries, or all these other places where people lack the resources to be a part of that sort of equity of education that we might find in more developed countries, right?

Justin Reich (32:01):

Well, and certainly even within what people call developed countries, there enormous pockets of poverty and inequality. And there are many schools in the United States that have inadequate resources. I think you asked this sort of crucial question, which is, when we think of all the things that we want school systems to do, what are computers already good at? You talked about language learning before, computers aren't good at helping people facilitate a conversation about Cervantes, Don Quixotes. They're actually pretty good at having people practice conjugating verbs. And if we can use apps that help you develop some fluence and mastery in conjugating verbs, then we can have some teachers who have to spend less time helping students conjugate verbs, and students who can get faster on their own or with the technology support conjugating verbs. And ultimately that will hopefully lead them to in a faster and more effective way. Be able to have really rich conversations about Don Quixote.

Justin Reich (32:59):

There are also cool systems that are not just about sort of individually optimizing, algorithmically optimizing, each individual's learning pathway. But by using the connections that are available online, to have people discover their interests and pursue them. So, one of the things I talk about in the book, my colleagues at the Lifelong Learning Lab developed this programming language in community called Scratch. Which is a way that people can express computational creativity without having to learn the syntax of computer programming language. And there are people who have very deep, very rich learning experiences, inside the Scratch programming language. In my mind, it's a really sort of worthwhile thing for humans to develop this sort of computational creativity.

Justin Reich (33:49):

The notion of computational creativity wouldn't have existed in 1950 or 1960. And in 2020, it's still not sort of well established in schools. So if you want to take advantage of the Scratch programming language, you have to work with communities of people to redefine what it is that we think is valuable in schools to say, "Hey, let's add computational creativity to that, I think that's a worthwhile thing to do." But actually our schools are pretty busy from 8:00 AM to 3:00 PM every afternoon. So if you add so new, you've got to find something else that you want to take away.

Justin Reich (34:23):

There's a lot of worthwhile things to know. These very quickly become... It was very difficult, it was enormously difficult to solve the technical problem of, how do you build a programming language and community that helps people, relatively independently, improve computational creativity? It is, as hard as was, a far more challenging problem of, how do you reorganize the structure of our schools? The structures of our curriculum, the structures of our teacher training, the structures of daily school time, in order to be able to take advantage of those new innovations? Those things take a lot of time. That's the heart of the book, is that we can make these things better, but it's not new tools by themselves that do it. It's a combination of new tools, and social and organizational change. And social or organizational change is hard, and takes time, and takes, committed bodies of advocates to make those changes.

Alexander McCaig (35:17):

So then the future would be dependent on engagement, time, and large amounts of data collection, correct? So that these systems can continually be refined, and better, and then reworked and become more efficient into these models, wherever piece or place that I guess they would belong. Under the command of the superintendent, or whoever it might be that would determine that?

Justin Reich (35:44):

So whether or not they would depend upon large data collection. Most system's kind of naive data collection, is often actually surprisingly less helpful than you would think. What advances new technologies is not just data collection, but a sort of thoughtful research agenda that says, well, we're trying this right now. What if we modify the system somewhat, the people, the technology, other things, to do that. So that would be one clarification. In our particular society, not a lot of things happen under the direct command of a superintendent. So most school districts are run by democratically elected school board bodies, that are accountable to their communities. And they hire superintendents, who then hire principals and teachers. Those teachers, I think in our very best systems, are treated as professionals and granted a certain amount of autonomy.

Justin Reich (36:40):

So just like you wouldn't say that doctors operate under the command of a hospital CEO. Doctors operate under the direction of a hospital CEO, and then they use quite a bit of their professional discretion to make good decisions in local contexts. Teachers operate in somewhat the same way. But the general trajectory after you describe of, we make some technologies, we see how they're working, we see how they're working in systems, and we sort of constantly tinker with those systems to try to make them better. Some of the ways we make them better is by improving technology. But some of the ways that we make them better is by improving teachers, by improving school boards, by improving superintendents.

Justin Reich (37:18):

Our most powerful technologies that can conceivably really sort of change the way learning could happen. They usually don't work sort of just on their own, they require changes in curriculum. They require changes in professional development. They require changes in schedules. For our tools to be really powerful, our systems need to keep evolving with them. And the change of those systems is a political and organizing project.

Alexander McCaig (37:43):

Interesting. So you have to tinker politically, but also economically, and at some sort of humanistic level at the same time, to really make any of the magic happen then?

Justin Reich (37:54):

Yeah.

Alexander McCaig (37:55):

Interesting. Very interesting. So then, I mean, if I were to take anything from it, people should probably find the best way to tinker, and continue to tinker, rather than say, I'm just going to rip the whole wheel off the bike and put a new one on. I'd rather just work on one little spoke at a time.

Justin Reich (38:13):

Yes.

Alexander McCaig (38:15):

Is that sort of the fundamental outlet for how that would come to a close?

Justin Reich (38:18):

Yeah. I mean, I think the challenge of it is that sometimes I think about as a metaphor of levers. If you want educational systems to change, you usually have to pull sort of 12 different levers. You have to have a better assessment system. You have to have a better technology system. You have to have better human capital training. You have to have better school buildings. You have to have better schedules. We don't actually have the capacity to change all of those things at once, because students show up every day. So in fact, what really good changers and reformers are able of doing is, okay, I'm going to change this technology piece now. And it's not going to lead to all the benefits that I really imagine, because I haven't pulled those other 11 levers. But it's going to make something a little bit better right now, which is going to give me the capacity to change that again.

Justin Reich (39:03):

If we were to use your bike metaphor, we are eventually going to change the wheel. We are eventually going to change every spoke, and change the rim, and change the tire. But it turns out you can't just take out the wheel, because the bicycle is running. The bicycle is going down road each way. So you take out one spoke, and then another spoke. And then somewhere in there you change the rim, and you change the tire, and you change all the other spokes, and then you have a new wheel. My colleague, Ken Katinger says that, step changes what 25 years of incremental change looks like from a distance.

Alexander McCaig (39:35):

Yeah, no, no, that makes sense. And so then, to put this in layman's terms, for many people across the globe who listen to this. What is it that you would want to leave them as a final note in regards to your book, your work, and where you actually see technology taking a really positive stance for advancing education, or even making it available to them in their own countries?

Justin Reich (40:04):

I think the most important message is that technology can play a powerful role in improving our human development systems. But the folks who develop new technologies have the unfortunate proclivity to argue that these new technologies will be transformative, disruptive, will sweep away the past and bring in new futures. And that's not the way that technologies are going to make schools better.

Justin Reich (40:29):

Technologies are going to make schools better through a process of incremental improvement that doesn't come just from the technology. Every technology solution is a human capital problem. Every time we try to introduce new technologies, it has to be accompanied by supports from the humans who are going to implement those new technologies. Which means it's a process of systems change. And we can do that. It requires a certain amount of patience, because our human development systems are hard to improve and take a long time. But technologies can make them better. And in organizing our politics, are our other factors that are what make them better.

Alexander McCaig (41:09):

No, that makes sense.

Justin Reich (41:11):

[crosstalk 00:41:11] a layman's answer, but you know. If you call an MIT professor, you're probably not getting the best [inaudible 00:41:18] for giving you the layman's answer.

Alexander McCaig (41:19):

No, I think you did a fine job. Well, Justin, listen, thank you for coming on and helping me iterate through my thoughts after reading the book. And then also helping others really understand the current mode that educational systems, and the politics around it are in. And trying to create equitable base around people getting access to education at the same time. And hoping that technology can help advance that and close those gaps.

Justin Reich (41:47):

Well, I was very happy to be here. And I hope you have a terrific afternoon, and I wish the best to you and your listeners.

Alexander McCaig (41:53):

Thank you, Justin.

Speaker 3 (42:01):

Thank you for listening to TARTLE Cast with your hosts, Alexander McCaig and Jason Rigby. Where humanity steps into the future and resource data defines the path. What's your data worth?