When most people use this phrase, what they’re really discussing is either a) single-point adaptivity, which evaluates a student’s performance at one point in time in order to determine the level of instruction or material she receives from that point on, or b) adaptive testing, which determines a student’s exact proficiency level using a fixed number of questions.

When Knewton refers to adaptive learning, we mean a system that is continuously adaptive — that responds in real-time to each individual’s performance and activity on the system and that maximizes the likelihood a student will obtain her learning objectives by providing the right instruction, at the right time, about the right thing. In other words, while adaptive testing answers the question, “How do I get the most accurate picture of a student’s state of knowledge with a fixed number of questions?”, adaptive learning answers the question, “Given what we understand about a student’s current knowledge, what should that student be working on right now?”

To provide continuously adaptive learning, Knewton analyzes learning materials based on a multitude of data points — including concepts, structure, and difficulty level — and uses sophisticated algorithms to recommend the perfect activity for each student, constantly. The system refines recommendations through network effects that harness the power of all the data collected for all students to optimize learning for each individual student.