Personalized learning needs to die (or does it?)

no-grim-reaperThis post’s title notwithstanding, let’s get this out in the open right away: I’m a huge proponent of personalized learning … or as education thought leader Michael Horn puts it in verb form, personalizing learning.

I drew the title from the title and lead line of a Reimagine Education article by Associate Professor Michael Kasumovic of the University of New South Wales in Sydney. I’ll use this post to respond (in indented green) to the “personalized learning needs to die” case made by Professor Kasumovic (who is also the founder of edtech company arludo).

– – – – – – – – – –

Personalized learning needs to die
Learning and teaching will change in the digital age. Let’s make sure we shape it right.
By Michael Kasumovic | undated, but likely Nov 2017

Personalized learning needs to die.

I could sit here and argue that it needs to die because companies are trying to replace our most valued resource – teachers – with computers and algorithms that will sterilize learning. I could also easily argue it’s because privatizing education and giving learning data to closed corporations are the biggest mistakes we’ll ever make. But although I think these are both very valid reasons, I’m saying this for a much simpler reason: personalized learning is never going to work and we’re wasting our time.

This is an overly harsh characterization, ironically coming from the founder of a company that describes it mission no different than countless others in the space, including the ones that he paints with his broad brush. Yes, companies are trying to sell products and services. But that’s been true for decades—we used to call them books. You could just as easily say that companies are trying to provide teachers with better tools. And, the extent that those tools might allow a smaller group of teachers to serve a larger group of students, that lowers cost. And cost is big challenge in education.

As an evolutionary biologist, I know a thing or two about why we behave the way we do. If you look at our evolutionary past and our current society, it’s clear that humans are social creatures. This is one of the reasons that social games and apps have the most users and continue to be so popular. We crave connectivity with one another, for better or for worse. But personalized learning is the antithesis of social connectivity because it encourages isolation during one of the critical, formative periods of our lifetime.

Schools have been assigning homework for decades. Homework has mostly been a solitary task. And one whose efficacy isn’t supported by evidence. So, it’s not as if personalized learning is taking something that’s always social and making it less so. I agree that some, maybe even a lot, of social learning is valuable. But personalized learning isn’t antithetical to social, and it certainly isn’t about always being solitary.

I can see why people may think that a personalized approach can have an enormous benefit for learning. Imagine a student that is under- or over-performing in class, and I’m sure that you’ve imagined that they are bored and disengaged because they are either over- or under-challenged. Imagine then, that we could moderate which learning tasks an individual student receives so they are in that perfect zone – this flow – where they are perfectly challenged by an algorithm that knows exactly what they need. They are therefore driven to continue to want to learn. An appealing picture is thus painted– one in which every student can flourish.

But the reality is much different. Learning alone is daunting unless you have that internal drive that only someone with experience can have. We also know what happens to kids that are isolated as we’re seeing it more and more in this digital age: they become depressed.

Again, this assumes that personalized will have learners always be solitary. I don’t think they have to be any more solitary with personalized learning than they’ve been with homework.

At the same time, learning alone doesn’t prepare anyone for a job in the future as there is no job in the world where employees work alone. And as our future becomes more diverse, so will the teams we work with, meaning that social and networking skills will be of utmost importance. If our current political climate demonstrates anything, it’s that in their formative years, we should be spending more time socializing students so they realize the diversity of backgrounds people come from and what that means for the future that we’ll have together.

There are many jobs where employees spend considerable amounts of time working alone. That said, the socialization goals that Kasumovic lays out are laudable.

But there are two things that bother me most about personalized learning. The first is that machine learning algorithms can only be as good as the data that are used to train them. Early data by companies are often from a particular group of students – affluent and white. This isn’t because these companies necessarily target these groups, it’s because the schools that house these groups are the ones that can most afford to try something new and different. So what does it mean when early data collected only represents how a fraction of the population thinks and learns?

I don’t know how true the “affluent and white” statement is. And I don’t know which half of it is more relevant (my guess is “affluent”). Still, that’s a relatively straightforward fix to make–train machine learning algorithms on data sets from diverse groups of students.

And the second aspect is that an individual is more than the sum of their decisions and how they respond to a particular question at a particular time. That’s because the factor that underlines all these aspects is the thing that makes us human – our emotions. Our emotions alter how we behave, perceive the world, and perform. We could be the smartest person in the world, but if we are feeling down about ourselves, we can struggle to get through the day.

So, a good learning experience would be one that takes into account both the learner’s general ability and their current readiness to learn? That sounds, well, personal. Perhaps I disagree with Kasumovic because we have different definitions of personalized learning in mind. Unfortunately, in this regard, we’re left to try to read his mind (personal means solo learning guided only by a machine?), as he offers up no definition to support his case.

I’ve encountered numerous good definitions of the term–one that I particularly like is that of the Center for Collaborative Education:

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.

This is why having a human component in education is so important. Teachers, through a capacity for empathy amplified by years of experience with students, know when students need a hug more than anything else. They can tell that when a student is being disruptive, it’s not because they want to spoil others’ learning, but because it’s a cry for help. Humans are very in tune with one another’s emotions and empathy is sometimes what’s needed more than anything else.

I agree. But, as we’ve seen in medicine, machines are sometimes able to pick up things that people can’t. So why not craft a system that combines both?

By now you’re probably asking yourself: why should we bother with technology if it’s not going to help our students learn? I think technology can do this, just not in the way we’re thinking about it currently. And I think that technology can make the biggest impact in science by taking the fear away and replacing it with wonder, making the intangible clear, and speeding up the scientific process.

Kasumovic’s article continues with a non-objectionable description of children as “natural born scientists” before sharing his equally non-objectionable (and arludo-based) “vision of the future of education.”

Both accounts appear to lean to considerable extent on learning being, well, personalized.



How micro is too micro?

IMG_6495Kana Okano, Steve Nelson, Jakub Kaczmarzyk, Jeff Dieffenbach

As part of my work with the MIT Integrated Learning Initiative (MITili), I had the good fortune to be part of an MIT team that conducted “science of learning” research at the Masie Learning 2017 conference in Orlando.

With the help of 150 Masie Learning 2017 conference participants, an MIT research team set out to address the hot topic of micro-learning. The participants interacted with one of three variations of an elearning video on one of the conference themes, cybersecurity. The MIT team used EEG headbands to measure attention during the learning experience and a post-test and survey the next day to capture knowledge gained.

. . .

Taking a cue from one of the conference themes, cybersecurity, the MIT team created three elearning video variations: “Original” (the original 8-minute video), “Interrupted” (the same content as Original, but interrupted three times with short unrelated work tasks), and “1.5x” (the same content as Original, but increased to 1.5x the speed of the Original video.

. . .

The mean scores of the Original and Interrupted cases are essentially identical, with the 1.5x case trailing by a bit. However, the differences are not statistically significant–that is, none of the three variants can be said to have outperformed the others (the error bars represent the standard error of the mean).

. . .

The mean scores of the late morning case are the highest, with the early morning scores the lowest. The two afternoon means fall in the middle. The difference between the late morning and early morning scores are statistically significant, but the differences between late morning and both afternoon scores and between both afternoon scores and early morning are not (the error bars represent the standard error of the mean).

  • Full research report with photographs is here
  • Video of our project outline at the Sun opening session is here
  • Video of our presentation of results at the Wed closing session is here


As part of our conference research effort, my colleague Kana and I had the great fortune to briefly meet former First Lady Michelle Obama. In her keynote talk and in her brief conversation with us, she was the epitome of strength, grace, and thoughtfulness.


The crazy notion of free college


During the 2016 US Presidential campaign, populist candidate Bernie Sanders among others notably sounded the call for free college. Voices on the right quickly and repeatedly denounced the idea. With 4-year private school tuition bills having already shot past the quarter of a million dollars mark, any program government program providing for free college would clearly break the bank.

Or would it?

To answer that question, it’s instructive to deconstruct exactly what “free college” of the sort espoused by Senator Sanders would entail.

  • Sanders were not talking about private higher ed.
  • Sanders were not talking about out-of-state public higher ed.
  • Sanders were not talking about every expense–just tuition and fees.
  • Sanders were not talking about free college all–rather, need-based.

A back-of-the envelope calculation taking into account tuition and fees for need-based attendees of both 2-year and 4-year in-state public institutions yields an annual cost of less than $50B.

$50B may sound like a lot, but it’s less than 1% of our annual $7T government outlay. By comparison, the government spends on the order of 20% each on health care and social security/pensions respectively as well as roughly 10% on defense.

Maybe it’s not the money. Maybe it’s the notion of free. But is free education really all that radical a notion?

As it stands today, we already offer 13 years of free education. “Free college” in any practical sense merely adds another 2-4 years for a small subset of our high school graduates.

Education is the key to national competitiveness and societal advance. Investing a bit more in in effective higher education isn’t the crazy notion that it might seem.

The reading blip


Our eyesight has evolved over time. Same for our hearing. And our other senses. But a skill like reading? Natural selection simply hasn’t had enough time to do its work.

According to estimates compiled by Bruce L. Gary in his book “Genetic Enslavement,” only 5% of the global population was literate 500 years ago. The rate hit 10% in 1650, 20% in 1750, 50% in 1850, and currently sits at a 60% plateau first reached in 1900.

What will it take to jolt the system and restore the climb toward 100%? My bet is nothing.

In fact, I’ll double down and bet that a hundred years from now, fewer people … perhaps substantially fewer … will be reading in any sense that we currently associate with the term.

I won’t be surprised if reading eventually takes its place alongside arts like calligraphy as a practice of hobbyists but not a path to learning. As a means of receiving information, reading will give way to high speed video, immersive VR simulation, skill-on-a-pill, direct neural implant, or mechanisms we can’t yet imagine.

Don’t get me wrong–I love to read and have spend a considerable portion of my professional career helping educators help children learn to read. If I’m right and around long enough to see the decline, I’ll shed a tear.

But if you want to know what I think about the development, don’t come back here expecting an update. Instead, just tune your neuroport’s ultrabluefli scanner to //dieffenblog{history>arcane>reading}//.

Elements of effective learning


Multiple conditions contribute to successful learning. Intuition alone is insufficient to guide the understanding of these conditions—they must be backed by evidence. A key element of the mission of the MIT Integrated Learning Initiative (MITili) is to develop, collect, and share this evidence to improve learning.

“Making learning more effective” is too broad a challenge. To make the collection of evidence practical, MITili frames learning at three levels as shown in the illustration above: learner, instruction, and policy. Each of these levels can be broken down further to suggest research questions that can be asked and answered—the breakdown is not so much definitive as it is directional.

The learner, the instruction, the policy

The more that the learner, the instruction, and the policy are each set up to succeed, the more likely it is that learning will be effective.

learner1. The learner


If a learner has the right prior knowledge, motivation, interest, and physiological readiness, he or she will be in a good position to learn.

  • Prior knowledge
  • Motivation
    • Motivation as used here reflects external reasons for learning—to earn a credential, receive a promotion, or otherwise further a career. The topic might not interest the learner, but it could still be motivating.
  • Interest
    • Interest as used here regards internal reasons. There may be no external professional value to the topic, but it might still be intrinsically interesting.
  • Physiological readiness
    • The learner needs to be well-rested, well-fed, otherwise in good physical shape, and as important, in a good mental state that includes such traits as grit and growth mindset.

Having the right learner conditions in place isn’t sufficient for effective learning, however. Instruction must be right as well.

instruction2. The instruction

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

  • Content
    • Content includes the breadth, depth, and accuracy of the subject matter and the production value with which the subject matter is configured.
  • 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.
  • 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.

Having the right learner and instruction conditions in place also isn’t sufficient for effective learning. Policy must be right as well.

policy3. The policy

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

  • Law
    • Laws and regulations must be conducive to learning.
  • Access
    • Learners must have access to learning. This isn’t access via funding, but rather, access via circumstance. For instance, a K-12 student who is prohibited from going to a better school by virtue of geography/school/district boundaries might have an access problem.
  • Funding
    • Funds have to be available to pay for the learning experience. These funds may be provided by the learner or the provider of instruction.
  • Leadership
    • Leaders of those providing instruction must have a philosophy conducive to/supportive of learning.
  • 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.

This working paper outlines a framework—one that’s a work in progress—with which to think about specific research questions that can be asked and answered to improve learning effectiveness.

For instance:

Q: What impact does prior learning have?
Q: How can motivation be increased?
Q: What’s the role of better content?
Q: What’s the right duration for learning?
Q: How much funding is needed?
Q: What influence does leadership’s embrace of learning have?


Middle Run, White Clay Creek State Park, DESTEM–the combined educational disciplines of Science, Technology, Engineering, and Mathematics–“was first ‘coined’ as an educational term by the National Science Foundation (NSF) in the early 2000s.” [William E. Dugger, circa 2011]

Of course, science and math have long been part of the school curriculum. STEM, then, arose out of the desire to apply science and math in the form of technology and engineering.

More recently, a move has been afoot to introduce a more creative element to STEM in the form of Art. The result, alternately designated STE(A)M (somewhat patronizingly, I contend) or STEAM. Two sites that elaborate on this introduction are STEM to STEAM and STEAM Not STEM. As STEAM Not STEM’s home page suggests, the addition of Art is both for its value and to stave off the decline of art in our K-12 curriculum.

I’m on board with the spirit behind STEM and equally on board with the spirit by the extension to STEAM. But at some point, we run the risk of diluting the attention we’re attempting to draw.

With STEAM, do we really mean to exclude Reading/Literacy (the R in the titular STREAMS) and Social Studies (the S)? And once we include them (and foreign language, and electives, and …), aren’t we just talking education as a whole?

STEM Education is Dangerous?


Fareed Zakaria attempts in his book “In Defense of a Liberal Education” to make the case explicit in its title.

Based on his Washington Post treatment of the subject, he fails.

In a March 26, 2015 opinion piece, “Why America’s obsession with STEM education is dangerous” in the Washington Post, he writes:

Every month, it seems, we hear about our children’s bad test scores in math and science — and about new initiatives from companies, universities or foundations to expand STEM courses (science, technology, engineering and math) and deemphasize the humanities.

This dismissal of broad-based learning, however, comes from a fundamental misreading of the facts — and puts America on a dangerously narrow path for the future.

Zakaria’s critical error lies in his pivot from “liberal education” to what he asserts makes for a success in the global economy.

He cites the following as example elements of a liberal education: anthropology, English, philosophy, ancient Greek, psychology, and sociology. No disagreement there.

And he cites the following as example characteristics of a strong workforce: innovation, entrepreneurship, critical thinking, creativity, flexibility, social skill, confidence, self-esteem, problem solving, critical thinking, writing, design, marketing, and social networking. “Critical thinking is, in the end, the only way to protect American jobs,” he claims. Again, no disagreement.

Zakaria goes astray, however, in his inability to show liberal education as the better path to the important workplace characteristics he touts. These characteristics emerge as likely from a “technical” education as from a “liberal” one.

So, let the job market speak. Arguably, in today’s economy, technical jobs offer more openings and command better compensation than liberal ones (absolutely no substance slight to the latter intended). Look to medicine. Look to finance. Look to the biological and information sciences.

But should preparation for the workforce drive education’s primary purpose?

No less an authority than ASCD weighs in. Formerly the Association for Supervision and Curriculum Development, ASCD serves as “global leader in developing and delivering innovative programs, products, and services that empower educators to support the success of each learner.” In a July 2012 article “What is the Purpose of Education?,” they write:

In the United States, historically, the purpose of education has evolved according to the needs of society. Education’s primary purpose has ranged from instructing youth in religious doctrine, to preparing them to live in a democracy, to assimilating immigrants into mainstream society, to preparing workers for the industrialized 20th century workplace.

And now, as educators prepare young people for their futures in a world that is rapidly changing, what is the goal? To create adults who can compete in a global economy? To create lifelong learners? To create emotionally healthy adults who can engage in meaningful relationships?


Consider the skills that make one successful in the workplace. Critical thinking. Problem solving. Creativity. Teamwork. Communication. Unquestionably, these same skills form the foundation to rapidly change. To globally compete. To continually learn. To meaningfully relate.

Sadly, workplace success offers a tenuous hold in the current economy. A liberal education educates. It fascinates. But as an on-ramp to the workforce, its risk outweighs its reward.

“Job” may sound mundane. But try to imagine a more enabling power. A job puts water in our bodies. Food in our stomachs. A roof over our heads. Medicine in our cabinets. A job–at present, the province of the technical and not the liberal–provides the security and stability to contribute to family, community, and society in the most aspirational of manners.