Personalized Learning Workshop

2013

Office of the Provost

George R. Brown School of Engineering

Ken Kennedy Institute for Information Technology

STEMScopes

Denise FlyCharmaine St. RoseKathryn O’BrienCheryl MoreheadDaniel Williamson

one-size-fits-all learning

one-size-fits-all learning

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

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

personalizedlearning pathways

cognitive science research

big data

cycles ofinnovation

personalized learning

massive opportunity

but

challenges remain…

expensive

cost to develop one course supporting personalized learningcan exceed

$1M + several years

typically large team of disciplinary specialists to hand-codemeta data and rules

http://www.newscientist.com/article/mg21528765.700-the-intelligent-textbook-that-helps-students-learn.html

“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 platformsmachine learningcognitive sciencehuman-computer interactiondata security and privacy

scaling 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