Richard Baraniuk RDLS Rice center for Digital Learning and Scholarship Update

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 textbooks to 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 quality focus on learning written by professionals peer review and classroom testing sustaining ecosystem support project long-term proof of concept (phase 1)

digital open publishing platform founded at Rice University in open textbooks/collections 20,000 educational Lego blocks 40 languages >1 million users per month from 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 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

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 ecosystem disruptive 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 learners content 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) learners content 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 repositories machine learning cog-sci: how to optimize learning open: leverage global community

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

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 learners content data

textbooks that learn tech: digital repositories machine 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 Moon22% 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%

applications for 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) students concepts

applications for 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) student concepts

applications for 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 2012 ECE 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 findings 1.better retention and transfer of knowledge on an end-of- semester assessment relative to standard practice 2.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 1.OST > standard practice 2.effect size ≈ 1/2 to 1 letter grade deploying at GaTech, 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? learners content data

RDLS 3 Rice-based education projects gaining momentum OpenStax College STEMScopes Personalized learning Personalized learning broadens footprint from just outreach to cutting-edge research machine 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

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