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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. o ne-size-fits-all learning.

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Office of the Provost

George R. Brown School of Engineering

Ken Kennedy Institute for Information Technology



Denise Fly

Charmaine St. Rose

Kathryn O’Brien

Cheryl Morehead

Daniel Williamson

ed tech hype
ed tech hype

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 hype1
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
ed tech potential

data(massive, rich, personal)

close the learning feedback loop

p ersonalized learning
personalized learning


  • students and instructors as active explorers of a knowledge space
  • tools for instructors and students to monitor and track their progress


  • to each learner’s background, context, abilities, goals

cognitively informed

  • leverage latest findings from the science of learning

cycles ofinnovation




big data

cognitive science research

p ersonalized learning1
personalized learning

massive opportunity


challenges remain…


cost to develop one course supporting personalized learningcan exceed

$1M + several years

typically large team of disciplinary specialists to hand-codemeta 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.”

a doption chasm
adoption chasm

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

o ptimizing learning
optimizing learning

personalized learning systems can optimize learning

but what kind of learning?

e lectronic learning records
electronic learning records

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 privacyissues (FERPA, opt-out, …)

s cal ing up

large-scale platforms

machine learning

cognitive science

human-computer interaction

data security and privacy


David Kuntz, Knewton

David Prichard, MIT

Steven Ritter, Carnegie Learning

Break and Poster Session

Neil Heffernan, WPI

Winslow Burleson, ASU

a fternoon 1 2
afternoon (1/2)

JaschaSohl-Dickstein, Khan Academy

Anna Rafferty, UC-Berkeley

Zach Pardos, MIT

Richard Baraniuk, Rice University

Break and Poster Session

a fternoon 2 2
afternoon (2/2)

Panel: Data, Privacy, and Electronic Learning Records

Panel: Cognitive Science and Neuroscience in Personalized Learning


Breakout Reports