Richard Baraniuk

RDLSRice center for Digital Learning

and ScholarshipUpdate

agenda

• OpenStax College update

• Personalized learning update

• RDLS update

• Arnold Foundation and beyond

the goal

create a library of free, open-source learning resources that greatly expands access to high-quality learning opportunities

– student debt surpasses $1 trillion (NY Times)– 7 out of 10 students forgo buying texts (PIRG)

benefits:– improve college completion– improve student learning– positive disruption

proof of concept(phase 1)

$4.15m in venture philanthropy

5 open college textbooksto reach 10% market penetration

will save 843,750 students$83.24m through 2017 (20x ROI)

turn-key course solution textbook, mobile apps, ancillaries, homework system, analytics, …

high qualityfocus on learningwritten by professionalspeer review and classroom testing

sustaining ecosystemsupport project long-term

proof of concept(phase 1)

digital open publishing platformfounded at Rice University in 1999

1200 open textbooks/collections20,000 educational Lego blocks40 languages

>1 million users per monthfrom 190 countries

STEM content used 97 million times since 2007

technology development• Connexions’ XML/HTML5 OER platform provides

scalable distribution channel in multiple formats– HTML, PDF, ePUB, mobile, print

• By Fall 2012, books also available via Amazon, Barnes and Noble, iBooks, iTunesU, …

concept provedon-budget

– 2 books published in June; 3 to follow in early 2013– increased collaboration with CMU OLI on A&P

high quality– content professionally developed and peer reviewed– editorial boards with 21 luminaries, including

2 Nobel laureates, 2 former NSF Directors

high impact and efficiency– positive media reaction– 35 adoptions to date – $680k saved so far– College Physics has already paid back its $500k investment

growing ecosystem– 10 for-profit and non-profit partners

media awareness

“Big Savings for U.S. Students in Open-Source Book Program,” New York Times

“Free College Textbooks: Wave of the Future?” Forbes

“Rice University And OpenStax Announce First Open-Source Textbooks,” TechCrunch

“Why Pay for Intro Textbooks?” Inside Higher Ed

scaling up success

venture is exceeding expectations, but urgency to:

• keep the production line running smoothly (lowers costs and latency)

• add analytics and personalized learning functionality to increase student learning

• seize the narrow window of opportunity to disrupt the publishing industry

complete the library

$15.92m in venture philanthropy

20 additional personalizable open textbooks to reach 10% market penetration

library of 25 textbooks will save 1.6m students $156.8m through 2017

once phased-in, library of 25 textbooks will save students $782.9m every 5 years (ROI 39x)

sustaining ecosystemdisruptive force for good

textbooks that learn

personalized learning system that closes the learning feedback loop

new learning analytics(machine learning)

what works? what doesn’t?

massive open laboratory to study how we learn and really change how we teach

learning challenges

one size fits all

open-loop– students treated as passive receivers

of information– students poor at monitoring

their learning and often choose ineffective strategies

cognitively uninformed– activities that speed learning often

do not promote long-term retention or transfer

technology provides hope

personalized learning– adapt to each learner’s background,

context, abilities, goals

closed-loop– students and instructors as active

explorers of a knowledge space– tools for instructors and students to

monitor their progress

cognitively informed– leverage latest findings from the

science of learning

learn

erscontent

data

a long way to go

today’s personalized learning systems are

– proprietary (especially wrt data)

– expensive (limits access)

– fragile (based on rules)

– not scalable (limits access)

– focused on tech, not learning(creates a chasm)

learn

erscontent

data

textbooks that learn

a modern personalized learning system

– open (content, code, data)

– free (greater access)

– robust (based on machine learning)

– scalable (performance improves with more usage)

– focused on learning, not tech(crosses the chasm)

textbooks that learn

tech:digital repositoriesmachine learning

cog-sci:how to

optimize learning

open:leverage global

community

balance technology with cognitive science

cognitive science team

Elizabeth Marsh, DukeAndrew Butler, DukeHenry Roediger, WashU

“A Personalized Learning System based on Cognitive Science,” funded by NSF Cyberlearning Program, 2011

learning principles

• Retrieval practice– retrieving information from memory is not

a neutral event; rather it changes memory – “testing effect” is robust and replicable

• Spacing– distributing practice over time produces better long-term

retention than massing practice – “spacing effect” is extremely robust and replicable

• Feedback– closes the learning feedback loop– must be timely

learn

erscontent

data

textbooks that learn

tech:digital repositoriesmachine learning

cog-sci:how to

optimize learning

open:leverage global

community

machine learning for education

learning analytics– assess and track student progress– help instructors become better teachers– study what really works, what doesn’t– state-of-the-art machine learning– exploit massive data,

not hand-coded rules

scheduling– close the learning feedback loop– propose optimal learning path

for each student

(Peter Norvig)

Grade 8 science• 80 questions• 145 students• 1353 problems

solved (sparse) • 5 concepts

Concept 1:     Properties of Soil           52% Classifying Matter           26%      Earth, Sun, and Moon 22%Concept 2:     Evidence of the Past    57% Earth, Sun, and Moon     24%      Properties of Water         19%Concept 3:     Mixtures and Solutions 40%      Alternative Energy          34%      Changes to Land           26%Concept 4:     Alternative Energy       37%      Earth, Sun, and Moon      35%     Changes from Heat        28%Concept 5:     Properties of Water      54%      Formation of Fossil Fuels 27%      Earth, Sun, and Moon    19%

applicationsfor instructors

• Instructor dashboard to replace grade book– estimate and track student

concept mastery, on individual and class basis

• Automatic “concept map” – estimate problem difficulty and

identify good/bad problems

• Automatically group students into “eigenstudent” groups for remediation or acceleration

• Detect cheating and gaming

• Suggest what content student(s) should study next (scheduling)

studentsconcepts

applicationsfor students

• Student dashboard to replace grade book

• Feedback on individual problems(concepts involved, etc.)

• Identify strong/weak areas, including what to watch out for when studying

• Progress through the “course map”

• Relative standing in class

• Projected final grade

• Suggest content to study next (scheduling)

studentconcepts

applicationsfor admins

• Admin dashboard – tracks student progress– tracks and compares

instructor progress

• Estimate problem difficulty and identify good/bad problems (aids curriculum design)

• Predict scores on final exams/standardized tests

• Detect cheating and gaming

• Insights into higher-level demographic effects

experiments

Ongoing: Mturk with Algebra and OSC College Physics

Fall 2012ECE courses at Rice, GaTech,UTEP, RHIT

Rice Coursera courses (2)

STEMScopes (~700,000 students)

beta testing

ELEC301 Signals and Systems – homework replacement w/ cog sci

(feedback, retrieval practice, repetition, spacing)– no machine learning based personalization

preliminary findings1. better retention and transfer of knowledge on an end-of-

semester assessment relative to standard practice2. magnitude of the benefit was almost equivalent to one letter

grade considering completely accurate use of knowledge (no partial credit) and about half of one letter grade considering giving credit for partial knowledge

summary3. OST > standard practice4. effect size ≈ 1/2 to 1 letter grade

deploying atGaTech, RHIT,

UTEP, Fall 2012

impacts• Textbooks come alive!

– one size does not fit all in education– reinvent the entire process, making

it a continuous dynamic process of exploration

– close the learning feedback loop– open access for maximum impact

• A renaissance in computer-based learning– exploit the “unreasonable effectiveness of data”

– students will learn more effectively– instructors will become better teachers– everyone will better understand what works and what doesn’t– opportunity for cognitive science research at a massive, global scale

– the future of assessment?

learn

erscontent

data

RDLS• 3 Rice-based education projects

gaining momentumOpenStax CollegeSTEMScopesPersonalized learning

• Personalized learning broadens footprint from just outreach to cutting-edge research

machine learningcognitive scienceneural engineering (eventually)

• Impacts both outside and within Rice• Opens up new opportunities for fundraising from

“education minded” donors, especially K-12

arnold foundation

• Strong resonance with RDLS goals and activities

• “The Foundation works for transformational change in K-12 public education.”

• “Learning Systems: Developing and implementing innovative approaches to learning, including competency-based, digitized curricula with built-in assessments to permit students to learn anytime, anywhere and at any pace.”

• “Performance Management: Shifting the focus of accountability systems from compliance to performance; creating clear standards and transparent, accessible data to measure performance; and developing incentive and human resources structures that use this data to drive decision-making and improve quality.”

urgency• Know of other groups approaching Arnolds soon

regarding open textbooks and learning

• Proposal concept: Make Rice and RDLS the AF’s “research lab” for digital curricula, analytics, and personalized learning– short term

curriculum development (K-12, HE)– medium term

personalized learning system (OpenStax Tutor) massive open learning data archive

(first of its kind; can be a Rice/Arnold legacy)– long term

fundamental research in machine learning, cognitive science, neuroengineering, and beyond

budget thoughts• Star cognitive science chaired professor + startup $5m• Junior cognitive science faculty $1m• Star machine learning chaired professor + startup $5m• Junior machine learning faculty $1m• Nationally prominent Postdoc program $3m• Nationally prominent Grad student program $3m• Research funds $5m

• Personalized learning software tools $5m• Open data library $2m• STEMScopes/PL integration $1m• OSC/PL integration $1m• OSC library Phase 2 $10m• Support endowment $5m

• Total $47m• Compare to edX: $60m pledge from MIT and Harvard