In our recent EDUCAUSE Review article, Phil and I defined personalized learning as a set of technology-supported practices that help undepersonalize teaching. The three general practices that we identified are as follows:
- Moving content broadcast out of the classroom: Even in relatively small classes, a lot of class time can be taken up with content broadcast such as lectures and announcements. Personalized learning strategies often try to move as much broadcast out of class time as possible in order to make room for more conversation. This strategy is sometimes called “flipping” because it is commonly accomplished by having the teacher record the lectures they would normally give in class and assign the lecture videos as homework, but it can be accomplished in other ways as well, for example with reading-based or problem-based course designs.
- Turning homework time into contact time: In a traditional class, much of the work that the students do is invisible to the teacher. For some aspects, such as homework problems, teachers can observe the results but are often severely limited by time constraints. In other cases, such as comprehension of assigned readings, the students’ work is invisible to the teacher and can be observed only indirectly and with significant effort. Personalized learning approaches often allow the teacher to observe the students’ work in digital products, so that there is more opportunity to coach students. Further, personalized learning often identifies meaningful trends in a student’s work and calls the attention of both teacher and student to those trends through analytics.
- Providing tutoring: Sometimes students get stuck in problem areas that don’t require help from a skilled human instructor. Although software isn’t good at teaching everything, it can be good at teaching some things. Personalized learning approaches can offload the tutoring for those topics to adaptive learning software that gives students interactive feedback while also turning the students’ work into contact time by making it observable to the teacher at a glance through analytics.
Personalized learning is a set of things that you do, not a set of things that you buy. Products can support or enable personalized learning practices, but they are not “personalized learning products.” And in fact, once you have products that support personalized learning practices, that’s actually when the hard work begins. OK, so you moved content broadcast out of the classroom. Now what are you going to do with class time? Students get some benefit from having course content that they can review multiple times at home, but most of the big gains typically come from the teacher reclaiming class time for more high-value direct and interactive work with the students.
On the other hand, adaptive learning is a label that applies to products. Further, adaptive learning products can support all of the practice areas of personalized learning. They enable teachers to move content broadcast outside of class time, they make homework time into contact time through analytics, and they provide some tutoring function. “Adaptive” tends to provoke a lot of discussion around the latter of the three practice areas. See the piece I wrote for the American Federation of Teachers if you want a primer on the strengths and limitations of adaptive learning products as tutors. But as often as not the first two capabilities, neither of which is dependent on adaptive algorithms, are the ones that enable the biggest gains in personalized learning teaching practices. “Courseware” is a set of products that, when designed well and used properly, can enable faculty to move content broadcast out of the classroom and make homework time content time. Adaptive courseware adds the tutoring element while also, if done well, increasing the value of that homework contact time by providing better feedback through more targeted analytics.