This 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).
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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.