The adaptive learning engine that we’re building at Knewton is part of a larger trend of adaptive approaches to some of the world’s biggest problems. Rather than rely on rules that are etched in stone — rules that frequently have no bearing on the situation at hand — an adaptive approach responds to the environment. It reacts dynamically to what’s actually going on. It, well, adapts.
In New York, the grueling morning commute takes the average person 39 minutes. In Mumbai, it’s 47 minutes. In Tokyo, it’s 67 minutes! Think of all the energy, both personal and environmental, that’s wasted. At least part of the problem lies in how traffic signals are coordinated. You’ve probably experienced it yourself. You’re parked at a red light with a dozen other honking cars, wondering why the empty street before you has a green light. Sure, the traffic lights change according to a schedule, but why does it need to be that way?
Enter adaptive technology. These days, proposals abound on how to better control the flow of traffic. One team of Hungarian researchers has suggested putting wireless communication devices on cars and using the data culled from them to adjust traffic signals. Another proposal by a Belgian team involves making lights self-adaptive. By counting the number of cars on the road, traffic lights can determine whether they need to be green more (or less) often.
Both these ideas underscore the potential benefits of adaptive approaches. If a system moves from being strict and centralized to being fluid and atomized, its individual parts will function all the more smoothly.
At Knewton, we’re interested in applying the principles of adaptivity to LSAT prep and GMAT prep, and eventually to all learning experiences: By accounting for unique performance on individual concepts, we’ll be able to identify a study path that is tailored to each student. The usefulness of adaptivity in education is clear, and many real-world systems would see similar benefits.
David Yourdon is a Math Content Developer at Knewton.