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Why Students Don’t Like School, and What Adaptive Learning Can Do About It (Part 4)

Posted in Adaptive Learning on October 26, 2011 by

student ipad 006Miss Part 1, 2 or 3 of the series? Check it out here.

I recently read Daniel T. Willingham’s Why Don’t Students Like School?: A Cognitive Scientist Answers Questions About How the Mind Works and What It Means for the Classroom.

As I was reading Willingham’s investigation, I noticed that most of the real reasons Willingham argues that students don’t like school can be eliminated or reduced through continuous adaptive learning technology. In my first three posts of the series, I discussed ten ways in which adaptive learning can improve the classroom experience.

Here is one more reason that students find school distasteful – along with an explanation of how adaptive learning can help.

Lack of connection with expert work and outside world.

In my previous post, I described how dissatisfying it is to students when they feel like the hoops and hurdles they face are essentially arbitrary and culminate in nothing. Another factor that contributes to the sense that schoolwork is meaningless is the degree to which it is removed from expert work (real history, mathematics, poetry, science). No matter what their skill or age level, students wants to feel like their work matters and requires skill and focus. As Willingham argues, this detachment from expert work concerns educators as well as students: “If we’re not giving students practice in doing the things that historians and scientists actually do, in what sense are we teaching them history and science?”

Adaptive learning can narrow the perceived gap between school work and expert work in a number of ways:

A) Sheer Practice.

First and foremost, the difference between student and expert thinking is that for experts, space in what Willingham terms “working memory” is increased because experts have automatized many of their “routine, frequently used procedures.” This affords them cognitive energy to solve more complex problems. Professional physicists, for instance, don’t need to look up basic formulas, and professional ballet dancers can observe a complicated bit of choreography and immediately replicate the series of movements. This kind of automatization happens naturally when students receive enough practice: after hundreds of problems, students don’t think twice about the product of 7 and 7 or the order of operations in which an expression should be evaluated.

An adaptive learning system can speed up the process through which these basic and routine procedures are automatized, by determining each student’s exact needs and serving up problems designed to target weaknesses on a precise level. In other words, an adaptive system can help students use their time more efficiently, allowing them to see gains in their ability at a more satisfying pace.

B) Organizing Information.

According to Willingham, expert thinking is characterized by an ability to transfer knowledge between domains, “access the right information” in a swift and accurate way, and formulate productive questions and hypotheses about new information. What exactly allows experts to do this? As Willingham points out, “it’s not just that students know less than experts; it’s also that what they know is organized differently in their memory.” Experts store knowledge in a way that emphasizes deep, functional, abstract relationships. So, instead of thinking of things in surface terms, experts think about pattern and structure, how each part relates to the whole. This allows them to do all the things experts do: use acquired skills in new contexts, locate and retrieve stored information, and process new material in a productive way.

A sophisticated adaptive learning system can identify students’ “blind spots” and get them to organize information in ways that were previously alien to them. For instance, a student who has trouble seeing the big picture can receive questions or activities that guide him to think in larger terms; another student who has difficulty memorizing what seem like isolated facts can be shown how those facts relate to overarching ideas.

C) Exposure.

Many students simply don’t know what real scientists, mathematicians, writers, and historians are working on. In schools, we place a mild emphasis on helping students consider future career options (such as “doctor,” “teacher,” “judge,” etc), but we do little to expose them to mature manifestations of the academic work they’re actually doing. We tend to think that expert territory is too complex or niche-oriented for students, so when students ask us the point of school work, we appeal to them on an economic, vocational, and practical level. We say they’ll end up on the lower rungs of society if they don’t master algebra or try to persuade them that studying trigonometry will help them pursue their dream of becoming a lawyer. Why not stimulate student interest by trusting the “interestingness” of the subject itself to engage students, by answering questions like “what’s the point?” with a real, robust answer? Why not show them that the fields they’re immersed in are so interesting that adults are working on them, too, and that their work infiltrates our lives on a daily basis? (The iPad they’re holding, for instance, employs some technology developed and patented by working physicists.)

A sophisticated adaptive learning system can not only use student performance and activity to identify weaknesses and serve up problems that eliminate them; it can motivate and enrich student learning in a way that is equally precise. Because an adaptive system is computerized and involves tagged content, it can be hooked up to enormous repositories of expert material that normally lie beyond the realm of school (if those repositories are tagged). When appropriate, such a system can direct students to specific articles, studies, reports and books created by experts, for experts. (In this way, students can function as “apprentices” to experts, just as they did centuries earlier.) Adding even a slight degree of adaptivity to the sheer amount of digital content available has the power to significantly amplify the learning experiences we are currently familiar with.

A student who, say, demonstrates a facility with language can be introduced to the work of certain contemporary, practicing poets, and based on his preferences, be introduced to another set of poets and so forth. This experience is much more mature and individualized than working through a static, printed anthology that features a limited number of canonical poets. Companies already employ similar recommendation engines to figure out consumer preferences and recommend purchases; an adaptive learning system harnesses the same kind of technology for intellectual endeavor.

For a more detailed article on how we can teach subjects in a more mature and meaningful way, check out my post, Teaching Math Maturely.

D) Original Work.

The most salient difference between student and expert work is the fact that experts produce original work in their field. While we can’t expect students to match the quality of such work (though we may be surprised with what they produce given the right stimulation), we can guide them in a productive direction by exposing them to expert work and directing them to opportunities for them to produce and showcase their own work.

Because it processes thousands of data points on student performance, an adaptive system can help students find like-minded peers and organize communities of learning (just as it can organize cohorts in the classroom). These communities might form within individual classes or schools or across districts and beyond. By tapping into arenas where they can learn from peers, showcase their own work, and receive feedback (from those who have no interest in grading them), students get into the habit of professional academic exchange at an early age.

Realizing that their work has an audience beyond the teacher is enough to motivate some students to engage deeply with their studies. And, oddly enough, participating in this sort of community is a more realistic vision of real academic work than the model we currently provide. It happens to be more satisfying and more productive as well.

To learn more about Knewton adaptive learning, download the whitepaper here.