“Have you heard of IXL? I love IXL, it’s so easy, it makes me feel so smart.” – Student
IXL is a computer-adaptive website that many teachers use for skills practice. I have nothing in particular against it. I do think that, more broadly, computer-based personalized learning platforms and the way they are used can fall into the trap of chasing what students like, rather than what’s best for their learning.
Here is an excerpt I often come back to on the science of desirable difficulties in learning:
Not long ago, the California Polytechnic State University baseball team, in San Luis Obispo, became involved in an interesting experiment in improving their batting skills.
Part of the Cal Poly team practiced in the standard way. They practiced hitting forty-five pitches, evenly divided into three sets. Each set consisted of one type of pitch thrown fifteen times. For example, the first set would be fifteen fastballs, the second set fifteen curveballs, and the third set fifteen changeups. This was a form of massed practice. For each set of 15 pitches, as the batter saw more of that type, he got gratifyingly better at anticipating the balls, timing his swings, and connecting. Learning seemed easy.
The rest of the team were given a more difficult practice regimen: the three types of pitches were randomly interspersed across the block of forty-five throws. For each pitch, the batter had no idea which type to expect. At the end of the forty-five swings, he was still struggling somewhat to connect with the ball. These players didn’t seem to be developing the proficiency their teammates were showing. The interleaving and spacing of different pitches made learning more arduous and feel slower.
The extra practice sessions continued twice weekly for six weeks. At the end, when the players’ hitting was assessed, the two groups had clearly benefited differently from the extra practice, and not in the way the players expected. Those who had practiced on the randomly interspersed pitches now displayed markedly better hitting relative to those who had practiced on one type of pitch thrown over and over. These results are all the more interesting when you consider that these players were already skilled hitters prior to the extra training. Bringing their performance to an even higher level is good evidence of a training regimen’s effectiveness.
Here again we see the two familiar lessons. First, that some difficulties that require more effort and slow down apparent gains — like spacing, interleaving, and mixing up practice — will feel less productive at the time but will more than compensate for that by making the learning stronger, precise, and enduring. Second, that our judgments of what learning strategies work best for us are often mistaken, colored by illusions of mastery.
When the baseball players at Cal Poly practiced curveball after curveball over fifteen pitches, it became easier for them to remember the perceptions and responses they needed for that type of pitch: the look of the ball’s spin, how the ball changed direction, how fast its direction changed, and how long to wait for it to curve. Performance improved, but the growing ease of recalling these perceptions and responses led to little durable learning. It is one skill to hit a curveball when you know a curveball will be thrown; it is a different skill to hit a curveball when you don’t know it’s coming. Baseball players need to build the latter skill, but they often practice the former, which, being a form of massed practice, builds performance gains on short-term memory. It was more challenging for the Cal Poly batters to retrieve the necessary skills when practice involved random pitches. Meeting that challenge made the performance gains painfully slow but also long lasting.
This paradox is at the heart of the concept of desirable difficulties in learning: the more effort required to retrieve (or, in effect, relearn) something, the better you learn it. In other words, the more you’ve forgotten about a topic, the more effective relearning will be in shaping your permanent knowledge (Make It Stick, excerpted from 79-82).
Part of my role in the classroom is to engage students in thinking about challenging ideas, monitor their learning minute by minute, day by day, and beyond, and connect concepts over time as we revisit them in more and more depth. I try to do all of that through the lens of a scientific understanding of how students learn. In 2017, too much personalized learning colors perceptions with the illusion of mastery and relies on making content feel easy as a substitute for substantive engagement, trading durable, transferable learning for hollow confidence-building and short-term skill retention.
I am interested in computer-based platforms for supplemental practice if they make my life easier, but personalized learning is far from where it needs to be to take on a primary role in the classroom.