Personalized Learning Workshop 2013

Office of the Provost George R. Brown School of Engineering Ken Kennedy Institute for Information Technology STEMScopes

Denise Fly Charmaine St. Rose Kathryn O’Brien Cheryl Morehead Daniel Williamson

one-size-fits-all learning

technology to the rescue!

Education Tech Investments Surpassed $1 Billion in 2012 As venture capitalists pour money into educational technology companies, some wonder whether they are just building a new bubble. ed tech hype

“Thanks to the invention of projected images, books will soon be obsolete in schools. Scholars will soon be instructed through the eye.” - Thomas Edison

ed tech potential data (massive, rich, personal) close the learning feedback loop

personalized learning closed-loop –students and instructors as active explorers of a knowledge space –tools for instructors and students to monitor and track their progress adaptation –to each learner’s background, context, abilities, goals cognitively informed –leverage latest findings from the science of learning

curriculum (re)design personalized learning pathways cognitive science research big data cycles of innovation

personalized learning massive opportunity but challenges remain…

expensive cost to develop one course supporting personalized learning can exceed $1M + several years typically large team of disciplinary specialists to hand-code meta data and rules

“While such results are promising, perhaps it's a little soon to crown Inquire the future of textbooks. For starters, after two years of work the system is still only half-finished. The team plan to encode the rest of the 1400-page Campbell Biology by the end of 2013, but they expect a team of 18 biologists will be needed to do so. This raises concerns about whether the project could be expanded to cover other areas of science, let alone other subjects.”

many educators/systems are reticent to changing their teaching methods wholesale or overnight yet many personalized learning systems require significant changes or training to use correctly adoption chasm

people want learning to be quick and easy

personalized learning systems can optimize learning but what kind of learning? optimizing learning

for machine learning, data is king new opportunity to study how people learn massive, global scale (millions of students) entire lifetime of learning (PK-24+) significant privacy issues (FERPA, opt-out, …) electronic learning records

large-scale platforms machine learning cognitive science human-computer interaction data security and privacy scal ing up

morning David Kuntz, Knewton David Prichard, MIT Steven Ritter, Carnegie Learning Break and Poster Session Neil Heffernan, WPI Winslow Burleson, ASU

Personalized Learning Workshop 2013

afternoon (1/2) Jascha Sohl-Dickstein, Khan Academy Anna Rafferty, UC-Berkeley Zach Pardos, MIT Richard Baraniuk, Rice University Break and Poster Session

afternoon (2/2) Panel: Data, Privacy, and Electronic Learning Records Panel: Cognitive Science and Neuroscience in Personalized Learning Breakouts Breakout Reports

Personalized Learning Workshop 2013