What's worked so far

Computer-based, self-paced learning systems have the potential to expand educational opportunities for untold millions of students, especially in places where human teachers are scarce.

But despite scores of new learning platforms, such as Brain Pop or Education Galaxy, current programs can’t offer any advice when a student gets bogged down and starts “wheel-spinning.”

Two Stanford researchers, working with a nonprofit that supplies tablet computers to children in crisis zones, have tested a machine-learning program that not only predicts when a student is likely to start 'spinning wheels' but also recommends a solution.

“People get stuck, which can be so frustrating, and they need outside help,” said Tong Mu, a graduate student in electrical engineering at Stanford who worked on the project. “A human teacher sitting next to the student can often figure out the right intervention, but teachers are spread too thin in many places and today’s systems don’t really offer that kind of help.” 

Read more

Developed at the Digital Initiative at Harvard Business School, Knewton's program stands out for its personalized feedback and instruction, leading to substantial improvements in student test scores. The program adjusts content to meet each student's unique needs, fostering a more engaging and effective learning experience.

Knewton is an AI-powered platform that leverages curated education content to tailor personalized lesson plans to students. 

Knewton has created a lot of buzz in the education space. It raised more than $180M in investment capital, and it has made more than 15 billion personalized recommendations to students since its founding in 2008.

In a study of more than 10k students, Knewton was able to show that demonstrating mastery of subjects by completing its program’s assignments was directly tied to better performance in courses overall. This benefit as even larger for students who were struggling, persisted over time to future assessments, and increased the pace of learning.

Read more