Richard Baraniuk OpenStax Courseware

courseware vision phase 1 – reinvent the textbook $$$

open digital content open ed publishing platform established in ,000 learning objects millions of users per month library of 25 free and open college textbooks addresses “access gap” for disadvantaged students 17 ecosystem partners 1099 adoptions, saving 300,000 students over $30M

courseware vision phase 2 – personalize the course

courseware vision phase 2 – personalize the course

1.broader access to high-quality courseware 2.improve learning using modern science (machine learning, cognitive science) 3.validation in real classrooms + research goals

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 practice, assessment, and learning 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 two-step answer process engages students in retrieval practice spaced concept practice timely, informative feedback

research verification 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 practice, assessment, and learning research

content learning analytics assess and track student learning progress by analyzing their interactions with content

content content analytics determine relationships among content elements

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 Judy Janet students problems

questions (w/ estimated inherent difficulty) concepts student knowledge profile Patty

data ML AlgsCog Sci personalized next task analytics to instructor feedback and analytics to student

curriculum (re)design personalized learning pathways cognitive science research machine learning cycles of innovation

crossing the courseware chasm The Mainstream Market Technology Enthusiasts VisionariesPragmatistsConservativesSkeptics

crossing the courseware chasm The Mainstream Market Technology Enthusiasts VisionariesPragmatistsConservativesSkeptics

long term impact “There is not such a cradle of democracy upon the earth as the Free Public Library” building the personalized courseware library of the future

sparfa

students problems sparse factor analysis Goal: using only “grade book” data white: correct response black: incorrect response grey:unobserved infer: 1.the concepts underlying the questions (content analytics) 2.each student’s “ knowledge ” of each underlying concept (learning analytics)

from grades to concepts students problems data –graded student responses to unlabeled questions –large matrix with entries: white: correct response black: incorrect response grey:unobserved standard practice –instructor’s “grade book” = sum/average over each column goal –infer underlying concepts and student understanding without question-level metadata

students problems data –graded student responses to unlabeled questions –large matrix with entries: white: correct response black: incorrect response grey: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 problems ~ Ber statistical model converts to 0/1 (probit or logistic coin flip transformation) estimate of each student’s ability to solve each problem (even unsolved problems) red = strong ability blue = weak ability

students problems + SPARse Factor Analysis ~ Ber

students problems + 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 problems 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 Judy Janet

questions (w/ estimated inherent difficulty) concepts student knowledge profile

technology architecture

marketing and adoption 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 scale-up — 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