Two stories from the American Association for the Advancement of Science annual meeting

Jeff Dieffenbach

jeff-at-aaas-feb-2019At the Fri MIT reception with TPPers Katie (’16) and Sarah (’18)

At first blush, neither of these stories fits a more formal definition of teaching and learning (the purported domain of this blog). But I learned a lot from both.


I’m down in our nation’s capital for the annual meeting of the American Academy of Arts and Science (AAAS). Heading into the opening expo Thu evening, the woman ahead of me asked the staffer if she could enter despite not having a badge yet (registration had just closed for the evening).

The staffer turns to a colleague and asks if it’s okay.

Colleague says to the woman, “Can you at least name a scientist?”

Woman replies with the best pro move ever, “Me. I’m a scientist. I’m speaking tomorrow morning.” Boom, mic drop.



Sunday morning at the MIT booth gracing the AAAS expo. The conference puts out coffee and tea service to help draw people into the expo area. Being a good environmentalist, I take my tea in one of the mugs and not a paper cup.

My colleague Bob similarly avails himself of the offered caffeine. In a similar mug. But coffee, not tea.

Amazingly, or maybe just oddly, I’ve never had coffee. I mean, never. A single drop had never crossed my lips. (Coffee was always a drink for grown-ups.)

Maybe you can see where this is headed.

Bob puts his mug down next to mine. Or maybe I put mine down next to his. I absent-mindedly reach for my mug and take a sip. But I’m distracted by a conversation Bob’s having with someone from NASA. An actual rocket scientist. Which, the rest of this story demonstrate, I am clearly not.

The distraction’s such that I only subconsciously notice how foul my tea has become. A second sip, though, and my frontal cortex is fully aware. I’m looking not at a crisp brown elixir, but rather, a cream-infused taupe sludge. I’m suave enough not to spit the vile brew all over the NASA scientist, so I swallow, grab my tea, and do a 60 second silentish gargle. That doesn’t quite do it, but two more do and I’m back to normal.

Scarred, but normal.

Back in Boston, I related the story to my sons. My older son, the teacher, sums it up this way, “You had lukewarm coffee. Generic stuff, from the conference food service. And with cream. You really couldn’t have done First Coffee any worse.”

Kid’s not wrong.


What does it mean for a team to learn?

Jeff Dieffenbach

Moose skin boat, Canadian Museum of History

A colleague of mine studies how teams learn, perform, and learn while performing. After a bit of mulling on this topic, it occurred to me that I didn’t really know what it meant for a team to learn separate from learning acquired from the individual team members.

I failed to adequately express my confusion and the conversation didn’t really get anywhere before dropping off both of our respective front burners. The question that I should have asked, but only recently formulated, is this: when a team disbands, in what form does team learning continue to exist?

Fast forward a year or two. In the course of the MIT Open Learning Journal Club that I lead with another colleague, we read the excellent Joint interactions in large online knowledge communities: The A3C framework (Jeong 2017).

The A3C framework posits that when individual interact, they may do so with varying degrees of shared–or unshared–goals, processes, and outcomes. The authors elaborate on four degrees of this sharing, from least to most: attendance, coordination, cooperation, collaboration.

Attending individuals have individual goals, processes, and outcomes. Imagine, for instance, a learner in a massive open online course (MOOC) interested in learning for himself but not in any way invested in the learning of others.

At the other end of the spectrum, consider a symphony. Yes, the individual members may have their own goals, outcomes, and even processes, but in the actual performance of the symphony, those individual wants are subsumed by melding of talents into a single piece of art.

By comparison, coordination and cooperation fall somewhere in between. The distinctions (unimportant for the topic of what team learning means) are illustrated in this table.

The article introduces the term “stigmergy,” the mediation of team interaction via artifacts. By virtue of the existence of an artifact (for instance, a job aid, training manual, or how-to video), it’s not necessary for all team members to participate in all facets of creating the artifact. Rather, they might contribute a specific part of the artifact along the way.

More important, the artifact serves future members of the team, or even members of different teams who subsequently encounter the artifact after the original team has disbanded.

What does it mean for a team to learn? It’s the collection of artifacts (including the documentation of the processes that arrived at those artifacts) created by the team in the course of carrying out its work.

postscript 2019-07-09: In doing some reading on and thinking about the topic, it occured to me that “team learning” goes beyond just artifacts to include the team members themselves, to the extent that they remain available to be asked by others for lessons learned.

VR is for learning, AR is for doing

Jeff Dieffenbach


The promise of augmented and virtual reality (AR and VR, respectively) is nearly unlimited. AR and VR won’t just be for entertainment, however … they’ll likely revolutionize learning.

There’s a tendency to think of AR (overlaying graphics onto a view of the real world) as a precursor to VR (a completely virtual world). For AR, think Google Glass and Pokemon Go. VR, on the other hand, conjures up more sophisticated, fully immersive video games (Fortnite, anyone) and books/movies such as Ready Player One.

When it comes to learning, I see the line between AR and VR differently. At risk of oversimplifying, VR lends itself to “learning before doing.” That is, one might use a VR simulation to practice a task in a safe space, for instance.

AR, on the other hand, promises to shine in the manner of “learning while doing.” Imagine an aircraft engine technician with a maintenance task in front of her. Rather than toggle her attention between a manual or video and the engine itself, she would be better served having an overlay of the engine schematic and the service steps projected onto the engine. In this way, her actions would be guided by the AR “assistant” without the need to disengage from then re-engage with the work at hand.

VR is for learning, AR is for doing.

Before the AI takes our jobs, can it help us learn?

Jeff Dieffenbach


AI in the Service of Learners and Learning

Mired in a problem with no obvious path toward a solution? Fear not, the conventional wisdom says, some combination of blockchain, the Internet of Things, and/or AI will bail you out.

Well, if that problem is learning, or more to the point, hurdles that block learning, AI may indeed help you navigate your way to better understanding. How might this work?

Right up front, let’s tackle the question of privacy that’s inevitably going to arise. To make the ideas that follow a reality, a hypothetical “learnerAI” is going to need to know about you. A LOT about you. For the purpose of this exercise, then, let’s stipulate that you, the learner, have complete awareness and control of your learnerAI. It’s there to HELP you, NOT to report ON you.

To aide your learning, what might your learnerAI need to know about you?

  • Your basic physiological information via biosensors
    • Heart rate, pulse, pupil dilation, galvanic skin response, attention, …
  • Your interests via direct survey
    • Technical topics, operational topics, financial topics, leadership topics, …
  • Your current work via job description and email, text, phone, and web monitoring
    • Roles, projects, tasks, …
  • Your career history via resumes, LinkedIn
    • Roles, projects, tasks, …
  • Your learning history via direct survey, calendars, stored credentials, and more
    • Degrees, programs, courses, conferences, books, articles, webinars, …
  • Your desired career future via direct survey
    • Roles, …
  • Your desired learning future via direct survey
    • Competencies, skills, …

Yes, your learnerAI needs to know a LOT about you.

With that information (and more, no doubt) in place, your learnerAI will be in a position to help you overcome problems at learner, instruction, and/or policy levels (a framework developed by the MIT Integrated Learning Initiative). We’ll address solutions at each of these levels in turn (in blue).


If a learner has the right prior knowledge, motivation, interest, and physiological readiness, he or she will be in a good position to learn. The learner isn’t always in the best position to judge the state of these conditions, however.

The learnerAI can help.

  • Prior knowledge: the learnerAI will use information about the learner’s prior learning experiences to make appropriate matches with content based on the learnerAI’s assessment of the level of that content.
  • Motivation: as used here, motivation reflects external reasons for learning—to earn a credential, receive a promotion, or otherwise further a career. The learnerAI will provide motivating context to the learner by mapping the content of a learning experience to current and future roles of importance to the learner.
  • Interest: in comparison with motivation, interest is intrinsic–typically, a learner either does or does not like a topic, although interest may certainly increase with familiarity and expertise. The learnerAI will draw on learner-expressed interests to match with the content in a learning experience when skill or process (and not content) is the primary learning objective.
  • Physiological readiness: The learner needs to be well-rested, well-fed, otherwise in good physical shape, and as important, in a good mental state. The learnerAI will detect these conditions and align learning experiences with the learner’s physiological receptiveness.


If the instruction has the right content, delivery, and assessment, the odds of effective learning improve.

The learnerAI can help.

  • Content: Content includes the breadth, depth, and accuracy of the subject matter and the production value with which the subject matter is configured. Content may cover knowledge and/or skills. The learnerAI, knowing a lot about what’s relevant to the learner, will uncover content addressing one of three needs: the learner knows that content is needed; the learner’s manager, peer, or subordinate knows that content is needed; and most important, none of the aforementioned parties knows that content is needed.
  • Delivery: Delivery variables include human vs. digital, synchronous vs. asynchronous, duration of learning, user-requested vs. pushed-to-user, and device through which the learning is consumed. The learnerAI will look at past successes and current needs to help deliver content when it’s most valuable. That timing might be in advance of a learning need (“learning before performing”) or in just-in-time fashion at the time of need (“learning while performing”).
  • Assessment: Assessment has to do with the frequency and depth with which the learner is assessed and the formative manner in which the responses are used to guide the next piece of content and delivery. A robust learnerAI will add fidelity and precision to the adaptive-branching inherent in effective “What’s next?” learning. Moreover, the learnerAI will track and record learning, both formal and informal based on the learner’s actions, calendar, and other input.


If the policy has the right law, access, funding, leadership, and measurement, the prospects for effective learning are better still. The learnerAI can help.

  • Law, access, and funding: Laws and regulations must be conducive to learning. Learners must have access to learning. This isn’t access via funding, but rather, access via circumstance. For instance, a job role that isn’t eligible for a particular type of training or isn’t in the right location for that training isn’t helped regardless of how good the training is. Finally, funds have to be available to pay for the learning experience. These funds may be provided by the learner or the provider of instruction. These three areas are included for completeness, but don’t really lend themselves to improvement via AI.
  • Leadership: Leaders of those providing instruction must have a philosophy conducive to/supportive of learning. Here, the learner AI WILL help–it can be tuned, and tune itself, to learning opportunities (content and delivery) in line with executive mission statements and vision and management implementations of those missions and visions.
  • Measurement: Measurement as used here is different from the formative level of assessment guiding the learner’s next instruction. Rather, it addresses the summative level of what learners come to know, how their behaviors change, and what organizational improvements those behavior changes drive. The learnerAI, through its tracking and reporting, will assist learners in demonstrating the value to their employer of the learning they undergo, thereby establishing a case to be made for future learning.


Learners will equip themselves with a learnerAI guide. Ideally, the learner will own this guide the way they own a LinkedIn account–it will not be provided by … and therefore accessible to … the learner’s employer. While learners may still feel pressure to share learnerAI data with employers and especially prospective employers, the learner ownership will mitigate the intrusiveness.

The learner will own, control, and tune the learnerAI to monitor physiology, job descriptions, to-do lists, communications, actions, and more. As a result, the learnerAI will assist with desired, assigned, and unanticipated learning. This learning will be delivered optimally: when the learner is ready to learn and/or when the learning is of the most value. In the background, the learnerAI will record learning for the learner to share when that sharing best benefits the learner.

The case against the case against “The Case Against Education”

the-case-against-educationI beg forgiveness for jumping into the Caplan-Ubell debate uninvited. But it’s such a great topic. And if nothing else, it’s such a great blog post title.

In a recent EdSurge article entitled “Why college is not an employment agency,” Robert Ubell, vice dean emeritus of online learning at NYU’s Tandon School of Engineering, takes George Mason University professor of economics Bryan Caplan to task for the latter’s book, “The Case Against Education.”

A caveat: I haven’t yet read Caplan’s book. If I were on balance agreeing with Ubell, I’m not sure that that would be fair. But since I’m on balance agreeing with Caplan.

My comments on Ubell’s article are in indented green below. 

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Why College Is Not an Employment Agency
By Robert Ubell | Feb 6, 2018

A new book makes “The Case Against Education.” It’s decidedly not something to give to a high-school junior looking to get into college.

College is important. It’s important because of its cost. And because of its opportunity cost. Anyone planning to attend college should do so with fully open eyes. I encourage readers to use the comments section of this post to indicate whether they would have done their college experience any differently. I know that I would have. At the time of my application to college, I was fortunate to have sufficient resources both to make the decision and to pay for the decision. And even still, I didn’t make the decision well. Because really, how many 18 year olds have the perspective to make such a decision well.

The author, Bryan Caplan, a professor of economics at George Mason University, draws a picture of college as a bleak and miserable place, a Dickensian ordeal, peopled with distracted students, taught by mediocre faculty who, apart from mathematics, science and English, have nothing worth teaching their bored and listless students. In his telling, higher education is all a big, expensive scam—such a dark place that you imagine that students are housed in a prison, not on campus. “The harsh reality,” argues Caplan, “is that most students suffer in school. Nostalgics who paint their education as an intellectual feast are either liars or outliers.”

It’s not clear to me the extent to which Ubell’s description echoes Caplan’s book or caricatures it (I know, I know, I need to give it a read …). If anything, college, at least for many, leans more Hedonistic than it does Dickensian. Are students distracted? Most likely–that’s a worthy problem to address. Are faculty mediocre instructors? It would be more accurate to say that they too often fail to receive training in and be measured on instruction. Is higher education a big, expensive scam? Well, it’s certainly expensive, as is screamed by the US’ nearly $1.5 trillion in student debt (almost twice that of US credit card debt).

Caplan says that a college degree is largely useless, claiming that it does not show that students learned anything useful for their future in the workplace. “We have to admit,” Caplan assures us,” academic success is a great way to get a job, but a poor way to learn how to do a good job.”

College degrees are far from useless, but at the same time, they are ripe for improvement. If nothing else, college degrees are a signal to the workplace of effort and completion. They are not, however, a signal of competencies gained.

Companies take it on faith that that a college degree is worth the handsome salaries graduates command; in contrast, dropouts suffer with little to show for their aborted time in school.

Craftily, Caplan pretends to discredit education because it’s a worthless training ground for industry, but his aim is more insidious, making the case for the withdrawal of state support from public education. “Stop throwing good money after bad,” he commands. “Cut education budgets. Shift the financial burden of education from taxpayers to students and their families.”

Here is where I depart from Caplan and side with Ubell. Higher education can be a better “training ground for industry” than it currently is. But in parallel with this improvement, the state should be doing more to support public education, not less [The crazy notion of free college].

Caplan, a conservative libertarian, doesn’t demand the same austerity from private or for-profit schools, but instead, he targets the very place where students from families without means can achieve something and can go on to live decent, fruitful lives. Caplan would deny them that opportunity.

In his argument against the efficacy of education, Caplan marshals impressive-looking pseudoscientific bar charts on almost every page, standing like tall, upright soldiers defending his claims. But in his unsupported case for privatization, curiously, his armor disappears. Not a single chart or graph is displayed showing the benefits of defunding higher education. That’s because the actual data would undermine his case.

I love Ubell’s turn of this phrase: “Caplan marshals impressive-looking pseudoscientific bar charts on almost every page, standing like tall, upright soldiers defending his claims.”

The nation has tried for-profit higher education and it failed.

The excellent documentary “Fail State” makes this case in compelling fashion.

“Publicly funded education has an awful track record,” Caplan claims, “wasting hundreds of billions every year.” However, shutting down state education is a disastrous idea, not only for reasons of ensuring equity in education, but also for its long-term effects on the economic health of the country. Universities are among the key driving forces in our thriving state economies—in California, Texas, Florida, New York and elsewhere—where colleges are the vibrant intellectual centers driving research and business development.

While Caplan dismisses the possibility that universities offer society any real economic benefit, data shows otherwise. After studying new data from UNESCO’S World Higher Education Database, covering 15,000 colleges and universities across 78 countries between 1950 and 2010, Anna Valero, a London School of Economics scholar, found that “the expansion of higher education from 1950 onwards was not just the product of growing wealth, it has also helped fuel economic growth around the world.”

Take a look at the 20 finalist cities in Amazon’s search for a second headquarters—universities are located at the heart of nearly every one. “All these places have something in common—nearby reputable universities that can churn out the young and the hopeful straight into Jeff Bezos’s welcoming arms,” observes Chris Matyszczyk, a consultant, in Inc.

America is supposed to be the Land of Opportunity, where a son of a Jewish tailor from an impoverished shtetl in Poland—as well as millions of other children from immigrant and other poor families—can go to college and learn “useless” things like poetry and art history, as I did at Brooklyn College, then a free city school. Neither poetry nor art history—a waste of time for Caplan—will get most graduates a job after graduation, but we should be proud of a society that educates its citizens broadly and not just trains them as docile workers.

Many other economists tell us that the solution to the coming crisis in the workplace is more education, not less. As Harry Anthony Patrinos at the World Bank reports, “Post-secondary education graduates are at the lowest risk of losing to automation. Those with high levels of education are less likely to be in automation-prone occupations. ”

But Caplan believes that the university fails completely in preparing students for jobs. His assumption is that higher education is the place where students should gain the skills they need to get them good jobs. But universities are not employment agencies. His mistake is that he confuses procedural with conceptual knowledge.

Here is where my pendulum swings back toward Caplan. While I love the idea of the luxury of spending four (or six, or ten) years in the full embrace of learning, we simply can’t avoid the practical impact of spending a quarter of a million dollars on a college education. For most people, higher education MUST prepare students for careers (which aren’t quite the same thing as jobs).

In my new book, Going Online, I clarify the difference:

“Procedural knowledge means knowing how to manipulate a condition or how to perform a task; for example, how to run a science experiment or solve a mathematical equation. Procedural knowledge is also a measure of our skills, tasks we know how to complete, and techniques we know how to follow. Training is designed to give workers procedural knowledge in order for them to do their jobs effectively. Conceptual knowledge, on the other hand, refers to our ability to appreciate major parts in a system, understand complex relationships, or categorize elements logically. At their best, universities are expected to equip students to excel at conceptual knowledge.”

Ubell’s examples of conceptual knowledge are of equal value to the workplace as his procedural ones.

Caplan smirks about the U.S. higher education dropout rate, arguing that his bored students are voting against college with their feet. “Excruciatingly bored students fill classrooms.” he laments. “Well, ‘fill’ isn’t quite right, because so many don’t bother to show up.”

But students drop out for all sorts of reasons. Boredom may be one, but surely it’s not the principal impediment that drives them away—a suspicious claim Caplan repeats continuously, vilifying students for slumping in their seats with ennui. But the most devastating reason why students drop out is not lethargy, but high tuition.

For most, college is a luxury product, equal to buying a Mercedes every school year at many private schools. One of the most shocking consequences of the steep price of higher education is that some 40 percent of students who actually get accepted don’t even show up because they can’t pay the admission price.

“College is a luxury product.” My point exactly.

Peterson’s college guide says that the number one reason students drop out is because of lack of funds to keep them going. “Many students take out school loans, but that isn’t always enough,” reports Peterson’s Brian Pivik. “Between the costs of classes, books, rent, and just trying to survive, students are more and more learning that while worth it in the long run, the cost of education is high.”

If Caplan’s book was your only guide to what matters in college, you’d never come across ideals that secure a more just and honorable society—that enlighten thoughtful citizens. In the brief section Caplan devotes to “values,” he dismisses them out of hand, claiming that higher education has little or no effect on conveying them to students. You’d conclude that education has no place in democracy. It’s only what you can take to the bank that seems to matter to the author. If you look up “democracy,” “ethics,” and “wisdom” in the index, you won’t find them. None of these ideas on which education has been founded since ancient Greece are even mentioned in passing.

It turns out, however, that education does play a decisive role in our democracy. Nate Silver, the data journalist who founded FiveThirtyEight, calculated the effect of voter education in the last presidential election. Soon after results were in, Silver studied all 981 U.S. counties to see how they voted, sorting the numbers by least and most educated, among other slices of the data, especially income and race. His conclusion? Education, not income, predicted who would vote for Trump and who wouldn’t.

As an economist, Caplan is surely familiar with “commodification,” a concept at the heart of Karl Marx’s case against Capitalism. Marx theorized that under Capitalism, everything is measured in terms of monetary value, even knowledge. Doubtless, Caplan teaches the concept to his students at George Mason University. In The Case Against Education, Caplan has so thoroughly embraced the idea, he is convinced that hardly anything taught in today’s classrooms has any intrinsic worth. Caplan takes commodification to an absurd extreme—that only skills that can be turned into high-paying jobs after college are of any value. The rest—art, music, history, literature—he deems worthless. If it weren’t so grotesque, it would be funny, more Groucho than Karl.

My fear is that Caplan’s prescription for American higher education will not be laughed off, but will be taken far too seriously. While Caplan believes he is a contrarian, expressing views thoroughly at odds with mainstream thought, regrettably, he is not alone. Jane Karr, former “Education Life” editor of The New York Times, warns that “State funding of public universities is on a track to reach zero in less than 20 years in some states and as soon as six in Colorado and nine in Alaska.”

State legislatures are already way ahead of Caplan, savaging state support for public education, shifting the burden from taxpayers to families—just as Caplan advocates.

Higher education faces three challenges:

1. The learning experience needs to be better aligned with the capabilities that career and life demand.

2. The instruction needs to be improved and measured.

3. The cost to the learner needs to be reduced.

Caplan likely agrees with the first and second, Ubell with the third. I’m on board with all three–these are addressable challenges, not ones from which we should run.

Is personalized learning a problem of privilege?


Paul Emerich France, a National Board Certified Educator, reading specialist, and classroom teacher in Chicago, wrote the article “Personalized Learning is a Problem of Privilege” for EdSurge. I’ll use this post to share my thoughts (in indented green).

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Personalized Learning Is a Problem of Privilege
By Paul France | Jan 21, 2018

Education is a dynamic space, with new trends constantly ebbing and flowing, the pendulum swinging back and forth between truly new innovations and recycled ideas. Experienced educators will recognize these patterns. But for younger teachers like me, whose careers are still in their infancy, it’s not so easy to see through the blinders.

When I moved to the Silicon Valley in 2014, I, like many, joined the gold rush to pursue this idea called “personalized learning.” I thought it was a panacea. I truly believed that tech-powered personalized learning could be the answer education was waiting for.

Call that what you want—a misguided, naive, idealist, arrogant optimism. Perhaps the idea of personalized learning as a panacea is all of those things—or none of them. But I’ve come to learn that the label “personalized learning,” or whatever the next big thing is called, doesn’t matter. What matters more is challenging the underlying assumptions and social structures that breed inequitable ideas that do not serve what teachers and students actually need.

I don’t regret my time in the Valley, though. It taught me some important lessons that I will take with me for the rest of my career. After three years there, here’s what I’ve learned.

I’ve learned that personalized learning doesn’t necessitate technology use.

We often conflate individualization with personalization. To sustainably individualize every child’s education, it helps to have the assistance of a complex technological algorithm to assign activities to children. But this happens at a cost. Using an algorithm to determine what children see is impersonal and dehumanizing. This approach focuses on consumption of educational material instead of interaction with meaningful provocations.

Without definitions, it’s impossible to gauge Mr. France’s use of “individualization” and “personalization” and how they differ from one another. That said, “assistance of a complex technological algorithm” is not necessarily the same thing as “using an algorithm to determine what children see.” First, the best definitions of personalizing learning are founded on educators being supported by technology, not replaced by it. Second, there may be areas where technology does an equal or better job (self-driving cars are a good analogy here), freeing up the educator to focus his or her time and energy in other ways. There’s nothing in any reasonable interpretation or implementation of personalized learning that “focuses on consumption of educational material instead of interaction with meaningful provocations.”

For context, let’s again turn to the Center for Collaborative Education’s definition of personalized learning.

Personalized Learning tailors the educational experience for every student by embracing individual strengths, needs, interests, and culture, and elevating student voice and choice to raise engagement and achievement. Personalized learning takes place within the context of educational equity, providing culturally responsive learning environments and equitable educational opportunities for all students.

I see why this way of thinking prevails, though, as I used to subscribe to it. The scope of skills taught in schools is relatively narrow, and at first, it’s reasonable to assume that the right arrangement of activities on a playlist, or the correct sequence of Khan Academy videos, could meet the needs of all children.

I’ve come to learn that this way of thinking is reductive, at best. It’s simply a more sophisticated version of an industrialized model for education, moving kids through a customizable assembly line, adding quizzes, games, and videos at different rates and in different orders.

It’s important to recognize that not all technology is bad. Tools that minimize complexity, make educators more powerful, connect individuals, or redefine learning tasks can contribute to a more personal learning environment. Tools like Seesaw help children create multidimensional digital portfolios and let their parents partake in their learning journeys; apps like iCardSort and Popplet allow children to explore abstract thinking; programs like Google Earth and Skype can connect faraway people and places, redefining what sorts of experiences can take place within the four walls of the classroom.

It’s just important to remember to ask ourselves why we’re using technology, and to make sure that it is making learning personal by amplifying our humanity, not limiting it.

Second, I’ve learned that personalized learning is a problem of privilege, and that education’s problems are mainly systemic.

Technologists and their wealthy funders often hypothesize that the problems afflicting education can be amended through digital tools. But many sometimes fail to acknowledge the role that privilege and inequity play in perpetuating injustice, and instead presume that tech tools that individualize will “close the achievement gap.” Schools in affluent communities can access these technology tools easily, while schools in low-income areas—and which, generally speaking, disproportionately serve communities of color—do not have access to these tools. But even if they did, I think they’d find that personalized learning is not a need at all, and that there are more pressing matters to address.

France’s argument here better supports the case for making effective personalized learning available to all than it does for suppressing the benefits of effective personalized learning for the privileged.

Many “personalized learning” tools don’t fulfill real needs. Rather, they serve perceived needs that have been fueled by privilege. Parents don’t need immediate, real-time updates on their child’s progress, and they don’t need their child’s education to be individualized. Modern society’s desire for instant gratification and boundless transparency has convinced us that these are real problems, when in reality, they’re simply socially constructed preferences.

I agree that parents don’t need “immediate, real-time updates on their child’s progress,” but I can’t see why they (and their children) wouldn’t benefit from effective individualized education (which is not remotely the same thing as “boundless transparency”).

What children need more are well-trained, well-compensated teachers who work in emotionally-safe environments where sustainability and humanity are valued above all else. But most schools are hardly able to pay teachers equitably, nonetheless train them to hone their practice, develop engaging curriculum or even use existing technologies effectively.

What if all the billions in private capital that support the edtech industry were matched by an equal commitment to supporting our educational infrastructure? I’d like to see that kind of money invested to create a sustainable system for teaching and learning, one that actualizes a democratic vision for education by combating privilege and promoting equity within and between schools.

Schools spend something on the order of 5% of their budget on curricular materials (including edtech and infrastructure). Even in a tech-intensive environment, that number won’t top 10%. Shifting a portion of that spend to support “our educational infrastructure” doesn’t provide enough funding to appreciably move that needle. And, technology offers the promise of helping to reduce the bigger piece of the pie—educator cost. With good edtech, I argue, a smaller number of better supported teachers will be able to get better outcomes at a lower cost. Are we there yet? No. Should we try to get there? Yes.

By neglecting to do so—and by choosing to invest in technology instead of people—we only deepen the divide between school districts, perpetuating compounding cycles of privilege and oppression that will only continue to widen the gap between high- and low-income schools.

Most importantly, I’ve learned that we need to work together.

There is no panacea or silver bullet that will solve the great problem of education. Relying on venture capitalism to solve perceived problems through tech-powered personalized learning only perpetuates systems of inequality, especially if only schools in high-income, predominantly white areas can access them.

No one is proposing that we rely on venture capitalism to improve our schools. Schools will improve our schools, in part through the traditional and digital education materials that they purchase. The funding model behind the organizations that offer these materials is immaterial.

No one idea, product or organization will be able fix it alone. This is the danger of the capitalist, winner-takes-all hero mindset. It hardwires self-interest within us, a self-interest that made me want to work in Silicon Valley. I wanted to be a 21st-century knowledge worker, and I wanted to hit it big by doing something cool in technology. Blinded by my own privilege, self-interest got the best of me. I focused too much on success in the education technology world and, as a result, began to lose fulfillment in the day-to-day of teaching. I felt disconnected and disempowered, and it was because I lost perspective on what really mattered.

We need to let go of the self-interest that capitalism has instilled in us. We need to work together and support each other, not perpetuate a theory laden with privilege for the purpose of capital gain.

In actuality, it’s the system that’s broken—not necessarily the people in it. I met incredible, intelligent people in Silicon Valley: teachers who were passionate, creative, and knowledgeable; technologists who thought radically differently than I did and pushed my thinking about what was possible in the classroom. But privilege and a capitalist mindset clouded our understanding of which problems really need to be solved in education.

It’s a well known adage in Silicon Valley to “fail fast.” As I tell my students, there’s nothing wrong with failing and being wrong, as long as you make a change and avoid making the same mistakes repeatedly. Education technology needs to learn from its mistakes, and I believe that getting back in touch with the principles of human-centered design will help education enthusiasts get back in touch with what really matters in schools. After all, people who know better, do better.

The central purpose of personalized learning–as implemented by educators, not providers of educational materials–is to get in touch with human-centered principles.

To be fair, educators also need to learn from their failures. One example is elementary reading. There’s clear research that shows how intervention can work best to help struggling readers. But too many reading teachers instead choose a “whole language” model that’s proven to be less effective.

It’s a flawed approach to criticize the new thing without taking a hard look at how well the old thing is working (or not).

To France’s original question, is personalized learning a problem of privilege? In my view no. Implemented well, personalizing learning offers the promise of helping to level the playing field, not tilt it.

Everybody gets an A


Grades in a World of Competency-Based Learning

On the one hand, we’re beset by the “Everybody gets an A” blight that is grade inflation. On the other hand, we have voices like that of Mike Barnes saying in Education Week, “No, students don’t need grades.”

So, what to do?

On balance, Barnes has it right. To be sure, there’s a need for accountability on the part of learners, teachers, and administrators. I won’t pretend that I know how to deliver on that accountability. Ultimately, though, we should be shifting formal education from a seat time model to one that’s competency-based.

In the seat time model, learners earn credit for each hour of learning. No real accommodation is made for people who start with very different levels of understanding. (One such accommodation, coarse as it is, is placing out of college classes via AP testing.)

Imagine two learners exiting a course that’s a prerequisite for a follow-on course. One learner has earned an A+, the other a C-. On paper, both meet the requirements for the follow-on. In practice, though, the A+ student may have mastered the content while the C- student barely scraped by.

What happens when even the best student only earns a B? The instructor might apply a curve to boost everyone’s grade. The objective should never be to compare students on an artificial curve–rather, it should be to help each learner to an absolute level of mastery.

In a competency-based learning (CBL) model, on the other hand, learners take as long (or short) as they need to demonstrate mastery. Within reason, CBL works. Learners don’t have an infinite about of time–if a learner who struggles takes years to master what most learners manage in months, the mastery bar may have been set to high.

I subscribe to a “competency-plus” model. Define mastery at a level manageable by most if not all learners in a practical amount of time. For learners who achieve mastery quickly, move them along to “plus” content in elective or optional areas.

If we equate mastery to the letter grade A, and if every learner achieves mastery, then “Everybody gets an A” isn’t grade inflation, it’s delivering on the learning objective.