Knewton’s goal is to be the “API for Education”—millions of pieces of digital educational content will run through the Knewton engine to create the Knewton Knowledge Graph. This graph will further identify billions of connections among learners, learning styles, content and instructional methods to personalize a learning pathway in almost any subject for any user. Additionally, Knewton aims to be the “LinkedIn of Learning”—every user on the Knewton Knowledge Graph should have a Learning Portfolio that tells the user how he or she learns best using the data created from the first day of using Knewton-powered content.
The global education industry is massive ($4.9T in 2015) and is undergoing a sea change with the move to digital and online instruction, materials and modalities. This shift to digital and online is enabling what many educators consider to be the “Holy Grail” of learning—personalized, adaptive instruction and assessment. Knewton’s personalized learning system has the potential to be the engine that powers this change.
Jose Ferreira is Knewton’s founder and CEO.
At its heart, Knewton is an API connected to a big data engine—learners use digital content and as they interact with this content, Knewton’s API ingests everything the learner does (mouse clicks, wrong answers, time to progress/process, et. al) and analyzes these learner actions using Knewton’s technology infrastructure to return to the learner an adaptive, personalized learning path to proficiency. Knewton can act as both a B2B2C and B2C learning platform, and should ultimately create a Learning Portfolio of every Knewton user’s personal learning styles. To date, Knewton has partnered with some of the world’s largest publishers—including Pearson, HMH and Macmillan—to deliver personalized solutions to learners.
Broadly, the global education market is enormous and rapidly growing. Knewton can capitalize upon the rapid growth rate of digital textbooks (estimated 35% by 2016) and the increase of blended learning in schools throughout the country (30% of U.S. learners in 2012, estimated to be 98% by 2020). Knewton is on track to power 10 million students on its platform by the end of 2014. We believe that Knewton can ultimately power hundreds of millions of learners globally over the next 3-5 years. As the major publishers all move their content to the digital realm, Knewton is poised to deliver personalized, adaptive learning solutions to learners on each of these platforms.
If Knewton ultimately becomes the “API for Education” the company could be one of the largest and most important in the world. Education touches every learner at every age, and is a massive global industry that Knewton is attempting to disrupt in a way that no other company has attempted over the past 50 years.
Knewton has already developed the world’s leading adaptive learning infrastructure and enjoys a meaningful “first-mover” advantage with respect to its people, technology and partnerships. By partnering with many of the major global publishers, Knewton is creating a substantial economic moat for itself, and should further enjoy tremendous network effects through its big data engine powering millions of learners around the world. Additionally, we believe that Knewton’s long-term contracts enable further defensibility of its economic model, and will contribute to the company’s already sizable network effects.
We believe Knewton needs to develop a B2C strategy in addition to its B2B2C strategy. To do this, Knewton may need to spend substantial sales and marketing dollars to reach consumers on this type of platform. Further, Knewton and its publishing partners will need to continue to prove the efficacy of “Knewton-powered” learning modules and courses. We should begin to see high-profile studies in the public domain over the next few years. If major publishers decide to create their own big data engines to drive personalized, adaptive learning, Knewton’s business model would be threatened.
GSVC percentage figures are based upon the fair value of each holding as of the quarter ended December 31, 2016, or the cost basis of the holding (exclusive of transaction costs) if the investment closed subsequent to December 31, 2016. In either case, these values are divided by the fair value of total portfolio investments of GSV Capital as of December 31, 2016.