VERSION 2TO DO

• Upgrade logos to high res

• Add in “demo” slides

• Add in “backup” slides

Richard Baraniuk Founder and DirectorDaniel Williamson Managing DirectorDavid Harris Editor-in-ChiefKathi Fletcher Product Manager

OpenStax Courseware

GOALS of OpenStax Courseware

1. broader access to high-quality courseware2. new tools to improve learning experiences

(machine learning, cognitive science models)3. validation in real classrooms + research

1. broader access to high-quality courseware2. new tools to improve learning experiences

(machine learning, cognitive science models)3. validation in real classrooms + research

today’s agenda

• OpenStax Courseware building blocks– digital content: Connexions + OpenStax College– digital assessment: OpenStax Tutor– cognitive science– machine learning

• key technology components

• content focus areas

• go-to-market strategy

• discussion and closing statement

digital content• open ed publishing platform

established in 1999• 25000 learning objects

in 40 languages• millions of users per month

• library of 25 free and open college textbooks

• professionally authored and peer reviewed

• 875 adoptions, saving 140,000 students over $14M

digital assessment

• in use at 12 colleges(Rice, Georgia Tech, Duke, UT El Paso, …)

• built-in research infrastructure

• integrated cognitive science principles(collaborators at Duke, UT-Austin, WashU)

• flexible platform for computer-based assessment and research

learning principles

retrieval practice– retrieving information from memory

is not a neutral event; rather it changes memory

spacing– distributing practice over time produces

better long-term retention than massing practice

feedback– closes the learning feedback loop– must be timely

learn

erscontent

data

digital assessment

• experiment at Rice 2012

• findings: Students using cognitive science principles in OST scored ½-1 GPA point better than those using standard practice homework

• flexible platform for computer-based assessment and research

learninganalytics content

analytics

learning/content analytics

classical approach – “knowledge engineering”– domain experts pore over content, assessments,

data, tagging and building rules– fragile, expensive, not scalable, not transferable

modern approach – “machine learning”– learn directly from data– automatic– robust, inexpensive, scalable, transferable

standard practice

Johnny

Eve

Patty

Neelsh

Nora

Nicholas

Barbara

Agnes

Vivek

Bob

Fernando

Sarah

Hillary

JudyJanet

standard practice

Johnny

Eve

Patty

Neelsh

Nora

Nicholas

Barbara

Agnes

Vivek

Bob

Fernando

Sarah

Hillary

JudyJanet

Goal: using only “grade book” data, infer:

1. the concepts underlying the questions (content analytics)

2. each student’s “knowledge” of each underlying concept (learning analytics)

students

pro

ble

ms

sparse factor analysis

• Goal: using only “grade book” data

white: correct responseblack: incorrect responsegrey: unobserved

infer:

1. the concepts underlying the questions (content analytics)

2. each student’s “knowledge” of each underlying concept (learning analytics)

students

pro

ble

ms

+students

concepts

each problem involves a combination of a small number of key “concepts”

each student’s knowledge of each “concept”

each problem’s intrinsic “difficulty”

~ Ber

Sparse Factor Analysis

questions(w/ estimated inherent difficulty)

concepts

studentknowledge

profile

87

55

23

93

62

Patty

DEMO SLIDES

technology architecture

content

• Principles of AccountingPrinciples of ManagementAmerican GovernmentMicrobiology

• content development in partnership with Words & Numbers (9 texts published to date, X in production)

• quality control via extensive peer review and classroom testing at partner colleges

• ROOM FOR ONE MORE BULLET

1.65 million students/year

go-to-market strategy• research partners will co-develop

– Salt Lake Community College, University of Georgia

• pilot partners will field test– The Ohio State University, Auburn University, University System

of Georgia-Online Courses, Central New Mexico College, South Florida State College, Maricopa CC District, Tarrant County CC

• key elements– fit into existing faculty/student workflow– build an ecosystem of affiliate partners– execute advertising and marketing campaigns– employ viral new media approaches– employ direct marketing and customer relationship

management system

• proven success 2012-2014

driving adoption: workflow principles

• Scope/sequence: content is available in complete discrete units of 100/200 level courses

• flexible: we provide for a blended learning experience

• part of the grade: assignable with metrics given

• APIs: interoperable across multiple platforms

access to drive adoption

• Institutional partners and pilots1. Salt Lake Community College2. University of Georgia3. Georgia University System4. Auburn University5. College of South Florida6. The Ohio State University7. Mariposa Community College District8. Shasta College District

• Large Base of OpenStax College Adopters1. Approaching 1,000 adoptions2. Over 135,000 student seats

Lumen Learning LogoCCOER Logo

Students and Faculty

Administrators and Faculty

Ecosystem Partners

summary – 1 What makes your proposed courseware “exemplary?”

– strong research base in machine learning, cog science

– 15 years of experience in digital education network of administrators/educators who already

use our content/tools

– once proven, can expand at minimal cost into comprehensive library of highest enrollment college courses

– strong backing of Rice University

– Not sure about this one: Flexible: works in multiple modes to meet various workflow requirements

summary – 2

How will your proposed courseware enable a “great leap forward” in improving the learning outcomes for low income, disadvantaged learners?

– platform integrates cognitive science principles that have been proven to improve knowledge retention and transfer large literature of laboratory studies Rice 2013 experiment

– machine learning learning/content analytics scale across courses dramatically lower cost/prices will result as compared to

courseware based on hand-coded ontologies

summary – 3

Why do you believe your team can develop your proposed courseware?

– experienced team has built

OpenStax College 140,000 students in 2 yearsPhysics textbook displacing market leaders

OpenStax Tutor 50 years of experience at IBM, Microsoft,JP Morgan Chase, Northrop Grumman,Texas Instruments, Cengage, Pearson, …

machine learning 20 years of research in Rice DSP group

– $72M in research, development, and deployment funding from 15 foundations and government agencies

summary – 4

How will you achieve wide adoption of your proposed courseware?

– proven go-to-market strategy most successful launch of a physics text

in 30 years (17.5% market share in 2 years)

– WHAT ELSE (remember this is a summary)

summary – 5 How does this project align to the charitable purpose set forth by the Foundation?

– Gates Foundation “guiding principles of Global Access”– The technology and products developed with grant funds be made

available and accessible at an affordable price to people most in need

Because of the relatively low cost (due to scalability enabled by machine learning), we can sustainably make OpenStax Courseware affordable to those most in need

– Knowledge and information gained from the project be promptly and broadly disseminated

As a university project dissemination is core to our mission; we have already published a number of papers in machine learning and cog sci

closing statement

curriculum(re)design

personalizedlearning pathways

cognitive science research

machine learning

cycles ofinnovation

closingAndrew Carnegie: “personalized courseware library” of the future

• Eric j appeal to carnegie – this is the “personalized courseware library” of the future

backup slides

backup slides

• More on tech• More on ecosystem/marketing?

budget

• Overview of the $5M budget and key categories

privacy

sparfa

from grades to concepts

students

pro

ble

ms

data– graded student responses

to unlabeled questions– large matrix with entries:

white: correct responseblack: incorrect responsegrey: unobserved

standard practice– instructor’s “grade book”

= sum/average over each column

goal– infer underlying concepts and

student understanding without question-level metadata

students

pro

ble

ms

data– graded student responses

to unlabeled questions– large matrix with entries:

white: correct responseblack: incorrect responsegrey: unobserved

goal– infer underlying concepts and

student understanding without question-level metadata

key observation– each question involves only

a small number of “concepts” (low rank)

from grades to concepts

students

pro

ble

ms

~ Ber

statistical model

converts to 0/1(probit or logisticcoin flip transformation)

estimate of each student’s ability to solve each problem(even unsolved problems)

red = strong ability

blue = weak ability

students

pro

ble

ms

+

SPARse Factor Analysis

~ Ber

students

pro

ble

ms

+students

concepts

SPARFA

each problem involves a combination of a small number of key “concepts”

each student’s knowledge of each “concept”

each problem’s intrinsic “difficulty”

~ Ber

students

pro

ble

ms

solving SPARFA

factor analyzing the grade book matrix is a severely ill-posed problem

significant recent progress in relaxation-based optimization for sparse/low-rank problems

– matrix based methods (SPARFA-M)– Bayesian methods (SPARFA-B)

similar to compressive sensing

standard practice

Johnny

Eve

Patty

Neelsh

Nora

Nicholas

Barbara

Agnes

Vivek

Bob

Fernando

Sarah

Hillary

JudyJanet

Grade 8 science

• 80 questions• 145 students• 1353 problems

solved (sparsely) • learned 5 concepts

Grade 8 science

• 80 questions• 145 students• 1353 problems

solved (sparsely) • 5 concepts

questions(w/ estimated inherent difficulty)

concepts

studentknowledge

profile

87

55

23

93

62

marketing

driving adoption: workflow principles

• Scope/sequence: content is available in complete discrete units of 100/200 level courses

• flexible: we provide for a blended learning experience

• part of the grade: assignable with metrics given

• APIs: interoperable across multiple platforms

access to drive adoption

• Institutional partners and pilots1. Salt Lake Community College2. University of Georgia3. Georgia University System4. Auburn University5. College of South Florida6. The Ohio State University7. Mariposa Community College District8. Shasta College District

• Large Base of OpenStax College Adopters1. Approaching 1,000 adoptions2. Over 135,000 student seats

Lumen Learning LogoCCOER Logo

Students and Faculty

Administrators and Faculty

Ecosystem Partners

charitable purpose

• mission of providing free access since 1999• free access for tutoring and review• anticipate a low cost, analytic driven, version

for classroom use, approx $10/student to maintain system