Posts Tagged 'personalization'

What Cognitive Tutors Can (and Can’t) Teach Us About Personalized Learning

Many educators, and especially those interested in educational technology, are currently obsessed with the idea of personalized learning. It’s at the heart of some well hyped initiatives such as the School of One in New York, in which students have tailored schedules, called “Playlists”, that guide them from activity to activity and computer algorithms that generate specialized lesson plans based on a student’s prior performance. The EU’s iClass experiment is also based on this idea of personalization via technology.

The basic promise of personalization is easy to grasp – not every child in a classroom is at the same level, and there’s (presumably) no way for a teacher to teach to all of these differences effectively. In the past, many  educators largely relegated personalized learning to those in need of remediation, the so called “low performers” in a class. This remediation often took the form of tutors, an expensive but effective approach. Later we also saw (and continue to see) Individualized Education Plans (IEP’s, in ed lingo), usually reserved for high risk or special education students. But the idea of having every student engage in some form of personalized learning, evidenced in initiatives like the School of One, may seem unique, but it is not new. Cognitive Tutors, computer programs that aim to replicate the effective guidance and adaptability that human tutors have been proven to provide, have been in the personalized learning game since at least the 1980’s.

Cognitive Tutors essentially incorporate cognitive models of both novice and expert thinking around a certain domain, like math, into a computer program. A learner is challenged to solve a problem that relates to that knowledge with the program providing hints but also taking into account multiple paths towards solving that problem as well as some common misconceptions that are represented in its novice models (Koedinger & Corbett, 2006). Most of the tutors that I’ve seen deal with math and science, and are predicated on the idea that there is one right answer to a problem, though potentially multiple paths towards getting to that answer. Even the most progressive (and impressive) amongst technology of this sort, Dan Schwartz’s Teachable Agents, which flip the model of the cognitive tutor by having the student school the computer as opposed to the other way around, are still predicated on there being one right answer to a particular problem. To me then, I see these as highly sophisticated ways to teach the basics, ie, the stuff that we as a society already know. But what about what we don’t know? Isn’t that the sort of thing that we need to have our future leaders grappling with?

This leads me to what I believe cognitive tutors can shed light on in terms of the model of technology and learning that I’m developing for a course I’m taking, a model I originally introduced and contextualized here and which you can interact with here. I’ve included a static image of the model for reference here:

One of the key innovations that I include in the model is this “Technology Driven Personalization System”, and it’s this idea that I think cognitive tutors can speak to, not because of what they do but because of what they don’t do. The general idea behind this personalization system, for me, is some kind of coordinating body that’s paying attention to all the “nodes” in a youth’s learning ecology and making recommendations for the young person about what might be best to pursue based on that from a learning perspective.

What I’m seeing in the proposal I’m making about personalization here is far less structured than how cognitive tutors conceive of the idea of personalization. It does not assume that there is one “right answer” as to the learning trajectory a learner should follow, indeed, it doesn’t envision an end goal. In contrast to heavily scaffolded learning technologies like cognitive tutors and many games (a technology I’m a fan of from an educational standpoint), what I’m envisioning is much more something that’s about resourcing the young person to pursue their own interests and their own values, as opposed to an imposed standard of what’s important to know. My model assumes that we must trust youth to become active learners, but doesn’t assume that they already have access to the tools and opportunities they need to do so. This is the role of the system I’m presenting here.

At the same time, I acknowledge that every system has its own politics and priorities, and so the question of what kind of  ideology is baked into the system is a very good one. Ideally, what I’d like to see is a system where the inherent ideology is itself  based on the idea of having others bring their own ideologies to the system and ‘make recommendations’ based on them.  Since many teenagers are often not quite at the stage of having very clearly articulated value systems and interests, I can envision the system integrating data about them in multiple ways, some more explicit (profiles with interests they’ve filled out, information about programs they’ve gotten involved with, classes they’re currently taking) and others less explicit (having some sort of match question system, common on dating sites, that don’t directly ask you what you’re interested in or how you think but rather pose situations or hypotheticals for you to respond to that then serve as indicators). All of this would then be integrated to make a profile of a given learner and what they’d like to pursue, which brings us, of course, to the issue of privacy and surveillance.

As someone deeply concerned about issues relating to exploitation and privacy online, my own proposal makes me nervous. Most of us are currently in a situation online where we’re not the customer in places like Facebook, Twitter and Google – we’re the product. Personal data is being packaged and sold to the highest bidder in the form of marketers, and governments are increasingly surveilling their citizens in these spaces. And it’s exactly the kind of personalization and recommendation engines that exist in places like Netflix, Amazon and Facebook, ones based on the existing data about a user, that I would imagine powering a personalized learning system of the sort I’m envisioning. That’s why it makes me nervous, and it’s also why the point I make above about politics and priorities being embedded in the system is so important – given the level of information that something like this would have about a young person it’s essential that it be clearly designed off of the principal of resourcing a young person to pursue their own interests according to their own values.

Finally, I’d envision the system incorporating some of the designs that drive Diaspora*, the open source social network that arose in response to Facebook privacy issues in 2010. In Diaspora, users have full ownership over their data, can share or not share to whomever they want, and simple ways to control privacy are put at the forefront. I would imagine the same, and more, for a system that would have so much data about a young person. And if I truly did believe in the idea of self-determination on the part of the young person, putting them in the position where they were in full control over their footprint within this system would only make sense.

A Model not for Technology in Education, but for Technology & Learning

For a while now I’ve been kicking around a hodgepodge of ideas about technology and its relationship to learning and education. Having worked in related fields for over five years now and gone to grad school to study more about this subject, I guess these are good questions to regularly ponder. Until  now though, I haven’t had a good opportunity to formalize these thoughts. As an assignment for the course “Computational Technologies in Educational Ecosystems“, we were tasked to create a model of our vision for technology in educational contexts, a really fantastic project that we’ll be refining over the course of the semester and that I’ll periodically post publicly about here on the old blog.

I decided to push the edges of the assignment somewhat, and rather than create a model for technology in educational contexts, I created a model for technology and learning writ large in the lives of youth. I’d be lying if I said the ideas here are all my own – for the most part, they’re a synthesis of ideas coming from emerging bodies of research and from colleagues I’ve worked with and been inspired by within the budding field of “Digital Media & Learning“, which in some respects positions itself as distinct from educational technology.

I share here I call a Youth Technology Learning Ecology, made up of a variety of “learning nodes” that youth interact with and which I believe can form better interconnections with one another in the future for the benefit of young people. What I’m really interested in is what digital culture and technology can offer us in terms of both inspiration for redesigning the learning systems that society has available for young people, as well as practical tools and practices that allow us to do that. I offer some initial thoughts on what a redesign of these systems might include.

To check out the interactive model, click the image below, which will take you to the Scratch website where you can interact with it. For best effect, I recommend enabling full screen.

I realize that right now not everything is totally clear in the model (it assumes some prior knowledge and some terms could use definition) and over the course of the semester I hope to refine it to clarify all of what I intend to be conveyed through it. In the process, I’m sure that the model itself will shift and evolve.

One of the big ideas in the model that I’d like to address is that of looking to “Interest Driven Affinity Spaces” (a fancy name for the places that kids geek out, online or off) as inspiration for reforming other learning contexts. I’ll start by referring to some of the readings that we did for the course this week, which offer some nice perspective on how people generally think about technology and education. In his classic book from way back in 1986, Larry Cuban shares an important insight about the ways that technology fads come and go in schools. The point is well taken. In looking to affinity spaces for inspiration though, I want to be clear that the model is not really advocating the integration of technology, even done thoughtfully, as one of many “passing fads” in schools, but rather for the rethinking of what counts as learning and what pedagogical practice and larger school cultures look like.

What’s hard to convey is that a shift to thinking about learning ecologies also implies a shift in our theory of learning, and both of those imply that schools need to be organizing themselves in much different ways. To engage in this reorganization, I believe that we can take a lot of inspiration from these affinity spaces that might considered “technology in the wild” (online communities, massively multiplayer games, fan sites, blog networks and many others) and what they do well, something scholars like Jim Gee and Mimi Ito have looked at in their work. The big idea about these spaces is that they provide youth with meaningful contexts and communities that not only keep them engaged and speak to their interests, but also are built around the development and learning of extremely complex practices and processes, have authentic and just in time feedback and assessment mechanisms as well as clear standards about what counts as “good work”. Schools rarely embody these qualities. This isn’t to say that we need for school to integrate these affinity spaces and their associated technologies, but rather to look at these spaces to see what makes them powerful learning environments and aim to bring those principles and characteristics into more formal educational settings. More importantly, I believe that we can not only shift the practices of any one setting, like school, but also the larger learning ecologies of which they are a part as well.

In his book, Cuban also makes a big assumption that anyone interested in technology and education is one way or another always going to point to technology’s ability to make content delivery more efficient. To me, this is off for a couple of reasons. First, it assumes that anyone interested in technology has an “accumulation of decontextualized bits of information” vision of what learning is, as opposed to one that uses participation in meaningful activities to foster dispositions, practices and processes that young people can tap in the future. Second, the ways that I think about “efficiency” deal mostly with reformulating pedagogy so that it’s actually effective by actively connecting to the other nodes in a child’s learning ecology. This is the second big point I’m aiming to get across in the model. Affinity Spaces are good to look to for inspiration, but there’s a huge opportunity to be tapped in aligning all of these various nodes a youth’s learning ecology so that they’re working together for the sake of that young person. This is where my (extremely underdeveloped) idea of a technology driven personalization system that accomplished this function comes from, an idea which I hope to develop more as the semester goes on. Importantly though, it represents a reframe of technology from being a “teacher’s helper” (or worse, their replacement), a view that starts not with the priorities of the formal educational system, which has consistently proven that it only values the transmission of decontextualized bits of information, but rather one that starts with the ways that youth are currently using technology in their everyday lives to further their own learning (though they rarely see it in these terms) and aims to connects these to all the other parts of a young person’s life.

Finally, another one of our readings validates this idea of looking to interest driven digital affinity spaces to inspire more effective pedagogy. Roschelle et al. (2000) [pdf] point out a number of processes that effective classroom technologies foster – active construction of knowledge, participation in groups, frequent feedback and connections to real world contexts. It is in fact just these kinds of processes that are at the heart of the kinds of deep learning activities that many youth are engaged in out of school through digitally mediated affinity spaces. The authors even reference these spaces, in the form of (now antiquated) electronic bulletin boards that dominated the early internet. Its heartening to see that in 2000, which is fairly early on in our current shift to a digital culture, there were already researchers that had identified practices technology can foster to provide more effective learning experiences.

I know that as it currently stands this is an incomplete model, and some things might be unclear, so feel more than welcome to leave a comment with any questions or thoughts and I’ll do my best to address them. And of course any critical feedback is definitely helpful on this first draft.


Hi there.

Rafi in thailand, smiling

If you're reading this, then you've reached the web log of Rafi Santo. This is my little slice of the internet where I can share my passion (or whatever) with the world.

Research. Meditation. Learning theory. Spirituality. Activism. Cooking. New Media. Pedagogy. Photography. It's all fair game, and will likely coalesce into some unholy mixture thereof. But hey, that's the integral life.

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