Why you should care

Becase maybe school shouldn’t be “one size fits all.” 

Want to hear about more visions of the future? Join OZY with Michael Moe at Stanford University and other C-suite executives from Slack and Dropbox on August 30 to explore The Future of Work.

The author is co-founder and chief investment officer of GSV Asset Management, which is an investor in OZY.

It was like walking into a time capsule, the principal said, when contractors at a high school in Oklahoma City stumbled upon century-old chalkboards during a renovation project this year. Almost perfectly preserved, the chalkboards held neatly marked math problems, arithmetic tables and multicolored drawings. The superintendent of the Oklahoma City Schools called them “artifacts” and vowed to preserve them at all costs.

The chalkboards are in good company. As it turns out, relics are pretty common at schools across the country, and schools have plenty of practice preserving artifacts. Chief among them is a one-size-fits-all education model, in which students are taught the same lessons at the same pace — regardless of their individual knowledge, skills and abilities.

We have long known that individualized instruction is a game changer. In a groundbreaking 1984 study led by psychologist Benjamin Bloom, students given personalized lessons performed two standard deviations better than their peers in a regular classroom. That’s enough to vault a middle-of-the-pack student into the 98th percentile. But despite Bloom’s findings, American classrooms have remained largely unchanged for nearly 200 years. One-on-one tutors, after all, are insanely expensive.

But that’s changing. Technology advancements that would have seemed like science fiction a decade ago have altered the fundamentals. We’re entering an age where every student can have a personal robot tutor to guide them through individualized education. It’s time to step out of the time capsule.

Infobesity vs. Smart Data

Today we generate a staggering amount of data. Eighty percent of the world’s data has been created in the past two years. In fact, the International Data Corporation predicts that by 2020 some 1.7 megabytes of new information will be created every second for every human being on the planet. To put that in perspective, in 1969, astronauts flew to the moon and back using computers with only 2 kilobytes (0.002 megabytes) of memory.

All this data has in some ways been a giant leap for mankind, but “infobesity” can paralyze. Separating the signals from the noise can be transformative. Indeed, advances in data science and database technology have made it possible to unlock insights from these vast troves of data, creating opportunities for a wide range of industries. E-commerce platforms like Amazon, Alibaba and eBay use powerful algorithms to predict purchase preferences and make timely product recommendations with precision accuracy. Spotify can suggest artists, albums and songs by constantly analyzing what music you — and people like you — listen to. More than half of the programs watched by Netflix’s 70 million–plus users start with a system-generated recommendation.

The list goes on. If you aren’t using big data, you have big problems.

Historically, it has been difficult, if not impossible, to access analytics that measure the effectiveness of a course or education product — not to mention to do so in real time or in a predictive or prescriptive way. But the digitization of education content — from printed textbooks to interactive software — has created the potential for personalized, adaptive learning materials. The same software that powers Amazon and Netflix can now track how well you understand a subject in real time while providing timely prescriptions to fill gaps and optimize learning.

Anatomy of Personalized Learning

When people go to the doctor, they generally expect to get a precise diagnosis and a treatment that addresses their specific malady. No one is satisfied with a partial recovery. Adaptive learning technologies aim to apply this same standard of life-altering precision and personalization to education.

Our firm views the hyper-personalization of education as an emerging tidal wave — and we’ve invested aggressively. For middle schoolers, there’s DreamBox Learning, which adapts at every click to create millions of learning pathways for mathematics. At the other end of the education spectrum is Declara, which provides people with personalized digital content recommendations — from academic journals to interviews and tweets — based on their personal interests and professional development needs.

Startups and established players alike are catalyzing innovation. Newsela, backed by Kleiner Perkins and Mark Zuckerberg, builds literacy skills with a publishing platform that automatically tailors news articles to a user’s reading level. Acrobatiq, a recent spinout from Carnegie Mellon, has developed adaptive courseware based on a decade of research from the university’s pioneering Open Learning Initiative. McGraw-Hill’s ALEKS and Pearson’s MyLab are high-impact personalized learning platforms that we believe will increasingly be adopted at scale. IBM is betting that supercomputer and Jeopardy! champ Watson will shake up education the way it has health care — with data-driven solutions — in recent years.

We believe the most ambitious vision has come from Knewton, a big data company that can diagnose what you know and how you learn best to pinpoint the best educational content for you. (We are part of a VC syndicate, including Accel, Bessemer Venture Partners and Founders Fund, that has invested more than $100 million in it). The recently launched Knewton.com, which is free to the public, can take any digital lesson — whether it’s created by a publisher or posted to YouTube — algorithmically calibrate it and bundle it on demand into a uniquely personalized learning sequence for any student. The field is still nascent, but early results are promising: When Arizona State University started using Knewton-powered developmental math courses, student pass rates rose quickly, from 64 percent to more than 75 percent. Nearly half of students finished the course four weeks early, and withdrawal rates dropped by more than 50 percent.

Our robot tutors have arrived, and so has the future of education. Now we just need to boot up.



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