Richard Baraniuk

1. RDLS Rice center for Digital Learning and Scholarship Update Richard Baraniuk

2. agenda • OpenStax College update • Personalized learning update • RDLS update • Arnold Foundation and beyond

3. 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

4. proof of concept(phase 1) $4.15m in venture philanthropy 5 open college textbooks to reach 10% market penetrationwill save 843,750 students $83.24m through 2017 (20x ROI)

5. proof of concept(phase 1) turn-key course solution textbook, mobile apps, ancillaries, homework system, analytics, … high quality focus on learning written by professionals peer review and classroom testing sustaining ecosystemsupport project long-term

6. digital open publishing platform • founded at Rice University in 19991200open textbooks/collections • 20,000educational Lego blocks • 40languages • >1 million users per monthfrom 190 countries • STEM content used 97 million times since 2007

8. 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, …

9. concept proved on-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

10. 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

11. 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

12. complete the library $15.92m in venture philanthropy 20 additional personalizable open textbooksto reach 10% market penetration library of 25 textbooks will save 1.6m students $156.8mthrough 2017 once phased-in, library of 25 textbookswill save students $782.9m every 5 years(ROI 39x) sustainingecosystem disruptiveforce for good

13. textbooks that learn personalized learning systemthat 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

14. 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

15. 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 data learners content

16. 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) data learners content

17. 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)

18. textbooks that learn tech:digital repositories machine learning cog-sci: how to optimize learning open: leverage global community

19. balance technology with cognitive science cognitive science team Elizabeth Marsh, Duke Andrew Butler, Duke Henry Roediger, WashU “A Personalized Learning System based on Cognitive Science,” funded by NSF Cyberlearning Program, 2011

20. 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 retentionthan massing practice • “spacing effect” is extremely robust and replicable • Feedback • closes the learning feedback loop • must be timely data learners content

21. textbooks that learn tech:digital repositories machine learning cog-sci: how to optimize learning open: leverage globalcommunity

24. 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)

25. 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%

26. applicationsfor instructors • Instructor dashboardto replace grade book • estimate and track studentconcept mastery, on individual and class basis • Automatic “concept map” • estimate problem difficulty andidentify good/bad problems • Automatically group students into “eigenstudent” groups for remediationor acceleration • Detect cheating and gaming • Suggest what content student(s) should study next (scheduling) students concepts

27. applicationsfor students • Student dashboard to replace grade book • Feedbackon individual problems(concepts involved, etc.) • Identify strong/weak areas, including what to watch out for when studying • Progressthrough the “course map” • Relative standing in class • Projected final grade • Suggest content to study next (scheduling) student concepts

28. 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

29. experiments Ongoing:Mturk with Algebra and OSC College Physics Fall 2012 ECE courses at Rice, GaTech, UTEP, RHIT Rice Coursera courses (2) STEMScopes(~700,000 students)

30. beta testing ELEC301 Signals and Systems • homework replacement w/ cog sci(feedback, retrieval practice, repetition, spacing) • no machine learning based personalization preliminary findings • better retention and transfer of knowledge on an end-of-semester assessment relative to standard practice • 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 summary • OST > standard practice • effect size ≈ 1/2 to 1 letter grade deploying at GaTech, RHIT, UTEP, Fall 2012

31. 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 renaissancein 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? data learners content

32. RDLS • 3 Rice-based education projectsgaining momentumOpenStax CollegeSTEMScopes Personalized learning • Personalized learning broadens footprint from just outreach to cutting-edge researchmachine learning cognitive science neural engineering (eventually) • Impacts both outside and within Rice • Opens up new opportunities for fundraising from “education minded” donors, especially K-12

33. 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.”

34. 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

35. 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