richard baraniuk openstax courseware. courseware vision phase 1 – reinvent the textbook $$$
TRANSCRIPT
Richard Baraniuk
OpenStax Courseware
courseware vision
phase 1 – reinvent the textbook
$$$
open digital content• open ed publishing platform
established in 1999• 25,000 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
spacedconceptpractice
timely, informativefeedback
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
learninganalytics assess and track student
learning progress by analyzing their interactions with content
content
contentanalytics
determine relationshipsamong 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
stu
den
ts
problems
questions(w/ estimated inherent difficulty)
concepts
studentknowledge
profile
87
55
23
93
62
Patty
data
ML AlgsCog Scipersonalizednext task
analyticsto instructor
feedback and analyticsto student
curriculum(re)design
personalizedlearning pathways
cognitive science research
machine learning
cycles ofinnovation
crossing the courseware chasm
The Mainstream Market
Technology
Enthusiasts
Visionaries
Pragmatists
Conservatives
Skeptics
crossing the courseware chasm
The Mainstream Market
Technology
Enthusiasts
Visionaries
Pragmatists
Conservatives
Skeptics
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
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)
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
questions(w/ estimated inherent difficulty)
concepts
studentknowledge
profile
87
55
23
93
62
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 2012-2014