Robot tutor helps to personalise learning
Yesterday a new, free tool for K-12 educators in America launched, aimed at making personalised learning easier. The tool, called Knewton, has been in development since 2008 and the company has spent seven years acquiring content that is now being given to educators for free. The way it works is very interesting: if a maths teacher is preparing to teach her students the pythagorean theorem she could invite the students to join Knewton, and assign them all a lesson about it. Instead of giving each student the same video though, the system would conduct an initial assessment to determine that student’s skill level down to the percentile. After the system determines the student's skill level, it will then mine through its content to determine the best resources for the student to learn from that point forward, hence, much more personalised learning.
Personalizing education can change a kid's life. It can keep the advanced ones from getting bored and the struggling ones from falling behind, because every kid, no matter their level of proficiency, is encouraged to move at his or her own pace.

But for the teachers tasked with implementing this increasingly popular pedagogy, it pretty much sucks. Personalizing a lesson means creating even more work than already time-strapped, under-resourced teachers can handle. It means drafting more lesson plans, digging up more reading materials, and creating more assessments than they would have to if they stuck to the old-fashioned, if imperfect, method of teaching to the middle. The sheer amount of work that personalized education requires of the educator is one reason why so few large and needy public schools haven't adopted personalized models at the same rate as their better-funded private school counterparts.

It's also one reason why Jose Ferreira and his company, Knewton, have spent the last seven years working on a way to fix that problem with technology. Today, they're launching the results of that work: a new, free tool that aims to automate personalized instruction for teachers.

To understand this new tool, you have to first understand what Knewton has been doing all these years. Founded in 2008, Knewton has spent the majority of its existence licensing technology to major educational content publishers like Houghton Mifflin. That technology allows those publishers to upload things like quizzes, reading assignments, videos, and more, and as students interact with the content, the system assesses how well each piece of content performs for a given type of student.

Meanwhile, the more students interact with the content, the more the technology learns about that student's proficiency in any given subject and what it takes to keep that student engaged. As the system learns more about a student, it can automatically recommend the pieces of content that are most likely to help that student improve. In other words, it lifts the burden of those decisions off of the teacher.

For Knewton, this licensing work has become a sizable business. It's accrued some 200,000 pieces of content over the years, which have been used by 10 million students in more than 20 countries around the world. Now, with this new tool, the company is unlocking the gates to this technology, by allowing any parent, student or teacher to access it for free.

"You always hear stories of teachers staying up until 2 am either writing their own content or finding it," Ferreira says. "Teachers don’t pay for it in dollars. They pay in time and labor and quality assurance. We’re taking that part and making it free."

Knewton

'The Magic Pill'

Let's say a math teacher is getting ready to teach her students the pythagorean theorem. The teacher could invite his students to join Knewton, and assign them all a lesson about the pythagorean theorem. Instead of serving each student the same video, though, the system would conduct an initial assessment to determine that student's skill level down to the percentile.

Then, the system would mine all of its content to find the one video, reading assignment, game, or other piece of content, that's best suited to that student. When the student's finished the assignment, the system gives her another mini-assessment to determine if it helped her grasp the concept. If it did, the student will advance on, and if it didn't, the system will find a slightly easier piece of content, until it finds a match. All of this happens without much teacher involvement at all.