Case study: understanding the power of personalized learning in action

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Teachers have for decades understood the importance of “differentiated instruction” – the process of tailoring instruction to meet individual learners’ needs. Thanks to advances in data science and machine learning techniques, as well as the shift of educational materials from print to digital, it is now possible to provide every student in a classroom with a more personalized learning experience.

Continuously adaptive learning systems can analyze each student’s performance and activity in real time, figuring out what an individual knows at any given moment and how they learn best. Additionally, adaptive learning technology can recommend what to study next to help individuals at varying levels succeed. Such a system can predict, with extremely high accuracy, how a student will perform on concepts before they’re introduced, and remediate her in advance to help prevent failure. Teachers can also access real-time reports about how students are performing, then shape lessons around concepts most students are finding difficult, and focus one-on-one time on each student’s needs.

In order to provide continuously adaptive learning, a system must analyze learning materials based on a multitude of data points — including concepts, structure, and difficulty level — and use sophisticated algorithms to recommend lessons for each student, constantly.

Knewton Math Readiness, a developmental math course built on the Knewton adaptive learning platform, provides just one early example of how personalized learning helps students succeed. As students progress through the course, Knewton technology analyzes data behind the scenes to continually assess students’ mathematical proficiency. The online course then recommends what a student needs to work on next, creating a continuously updated personalized learning path for each student. Instructors can see which students are off-track, search for individual student performance metrics, or view trends across an entire group of students to determine which concepts are most difficult across the board.

Early efficacy reports reflect the success of Knewton Math Readiness at Arizona State University. After four semesters of use with over 2,000 developmental math students at Arizona State University, withdrawal rates dropped by 56% and pass rates went from 64% to 75%.

To give a sense of how students progress through a unique personalized learning path, check out this visualization that showcases students going through one semester of Knewton Math Readiness at Arizona State University.

http://youtu.be/-NgZs9-oXQ4

Video key: The colors reflect the subject area within the course (equations and expressions, statistics and probability, algebra, functions, ratios & proportions, geometry, the number system) and each tiny white dot represents a different student moving through the course. The smaller circles within the larger colored circles reflect lesson components within the subject areas. As more students move through this subject material and gain proficiency, the circles are gradually shaded in. The timeline at the bottom of the visualization reflects the length of the entire semester; every second in the video is equivalent to 20 hours.

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