The Battle for “Personalized Learning”

So here we go again. Another terminology war. First there was the battle for open. Then the battle for MOOCs. Somewhere in there was the battle for edupunk.

I stay out of terminology wars because, even though they are often about very real and important issues, the emphasis on finding a single correct definition tends to distract rather than focus the conversation.

It’s a different with “personalized learning” because there is no fight over its meaning right now. Rather, it seems to have no specific meaning at all. Sometimes it is used interchangeably with “adaptive learning.” But not always. And not exactly. More often it means, roughly, “robot tutor in the sky.” Shorter version: “WHEEEEEE!!!” Or maybe, “Wingardium leviosa!”

Phil and I have decided to claim this prime piece of linguistic real estate. We are asserting squatters’ rights.

We hereby decree, by the power vested in us by nobody at all, that “personalized learning” shall henceforth refer to a family of teaching practices that are intended to help reach students in the metaphorical back row. The ones who are bored, or confused, or tuned out, or feeling stupid. Personalized learning practices are almost always ones that teachers have been using for a very long time but that digital tools can support or enhance. Here are a few that we have identified so far:

Move content broadcast out of the classroom: In many disciplines, the ideal teaching format is a seminar, in which students spend class time engaged in conversation with a professor. In others, it is a lab. Both models have students actively engaged in academic practice during class time, when the professor, as the expert practitioner, is present to coach them. Every class spent lecturing is a wasted coaching opportunity.

Many disciplines have traditionally used assigned readings to move content broadcast out of the classroom, and some still do. But it is not always possible to find readings that capture what you want to cover, and in any case, it is becoming harder to persuade students to read. Luckily, there are tools that can help with this problem. You can record and post your lectures as videos, which students can watch as many times as they need to absorb what you’re trying to tell them. You can assign podcasts that they can listen to on the go, or find interactive content that keeps them more engaged.

Make homework time contact time: Good teachers help students see the direct connection between the work they do at home and the overall purpose of the class. They do this in a variety of ways. Sometimes they mark up and comment on the student work. Sometimes they ask the students questions in class that require them to build on the work they did at home. For a variety of reasons, which often boil down to professors’ having less available time per student, this has become harder to do. The great crutch that is now being used to limp along without actually solving this problem is robo-graded homework assignments. By itself, automated practice might help some students drag themselves through to the end of the semester. But it doesn’t often inspire them to think that maybe they are not destined to be the student in the back row forever. (There are important exceptions to this rule, which I address below.)

On the other hand, these same automated homework tools can also give teachers an easy view into how their students are doing and create opportunities to engage with those students. “Analytics” in these tools are roughly analogous to your ability to scan the classroom visually and see, at a glance, who is paying attention, who looks confused, who has a question. Nor are these the only tools available for making homework time feel less isolated and pointless. Any homework activity that is done electronically can be socially connected. Group work done on a discussion board can be read over by the professor when she has time. Highlights and margin notes on readings can be shared and discussed in class. This sort of effort on the professor’s part doesn’t have to be exhaustive (or exhausting). Sometimes a small gesture to show a student that you see her is all it takes.

Hire a tutor: You know what tutors are typically good for in your particular discipline. You also know that there generally aren’t enough good ones available, and that even when there are, it’s tough to get students to come into the tutoring center. One of the best uses of machine-graded homework systems, especially when they are “adaptive,” is to treat them as personal tutors that are available to students whenever they need them and wherever they are. They aren’t perfect, but what tutors are? Sometimes getting students out of the back row means helping them to believe that they are capable of learning. And sometimes students are willing to pose a question to a computer that they would be embarrassed to ask in person. In those cases, a little extra practice and feedback on the basics, without judgment, can make all the difference — even if the feedback comes from a machine. And if adaptive learning robo-tutors don’t fit the needs of your students and your discipline, technology also makes it possible to connect students with actual human tutors, who are available online to help them get through the rough spots.

We have written about this idea briefly in The Chronicle as well as EdSurge and in detail in EDUCAUSE Review. We will keep beating this drum everywhere we can. We do not want or expect to create a definitive list of teaching techniques that define personalized learning. But higher ed needs a general term that focuses attention on student needs and teaching practices that can support those needs rather than on product features and computational plumbing. Since “personalized learning” is both widely known and nearly perfectly meaningless—all we know is that it has something to do with individual persons, learning, and technology—it seems like a good fit.

Furthermore, the vendors who have adopted the “personalized learning” mantle need this change as much as teachers and students do. While it is apparently possible to raise more than $150 million in venture funding on the strength of “robot tutor in the sky,” generating actual revenue from paying customers is another matter entirely. Faculty want to know what these “personalized learning” thingamabobs do, and they want to control how the products integrate into their classes. The evidence that this is so can be found in the massive, industry-wide pivot away from the xMOOC dream that one super-professor and big data can make all other versions of a course obsolete. Acrobatiq, a firm spun out of Carnegie Mellon University’s work on cognitive tutoring courseware that required a great deal of expert labor to hand-craft each one, now is touting an authoring platform for faculty. McGraw Hill Education, which has been promoting SmartBooks, has now released the authoring platform behind those products so that anybody can create their own.1 SmartSparrow, which has always been a DIY tool, is touting its
“free learning design tool” that “tries to make it possible for faculty to incorporate their own course materials in an adaptive learning environment.” Meanwhile, the Washington Post has declared, “‘Big data’ was supposed to fix education. It didn’t. It’s time for ‘small data.'”

Where have you gone, Joe diMaggio Thomas Friedman?
The nation turns its lonely eyes to you.
Woo woo woo!
What’s that you say, Professor Robinson?
Flat World Tom has left and gone away.
Hey hey hey!
Hey hey hey.

So I will say it again, not for the last time: Personalized learning is not a product. It is not a thing you can buy. If the term means anything at all, it must mean a set of teaching practices for reaching struggling or alienated students that many good teachers have known and employed for a very long time and that sometimes can be supported or improved through the use of educational technologies.

What do you know? It turns out the world is round after all.