online educa berlin conference: big data in education - theory and practice
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Online Educa Berlin Conference Presentation Big Data in Education - Theory and Practice Presented December 6, 2013 by Mike Moore, Sr. Advisory Consultant - Analytics Desire2Learn, Inc. Originally published http://www.slideshare.net/MikeMoore14/online-educa-berlin-conference-big-data-in-education-theory-and-practiceTRANSCRIPT
Big Data in Education:Theory and Practice
You have dataWe provide Insights™
Michael Moore, MSCISSr. Advisory Consultant – Analytics
Big Data – Big Deal
• Valuable insight about how we consume, behave and interact – Every click, tap, tweet, send
or swipe– Digital breadcrumbs
• Opportunity for personalization
• Big Data has Big Value– Organizations, institutions,
etc. can mine (use/extract) that data
What is so important about Big Data?
Big Data – Big Deal
• What qualifies as Big Data?• It is identified by the “3 Vs”
• How fast data is changingVelocity
• How much data there isVolume
• How many different kinds/sourcesVariety
Gartner - 2012
Big Data IS A Big Deal
• Identify Trends• Transparency and accessibility
• Connections• Seem arbitrary or incongruous
• Actionable Information• Decision Making
Why is Analyzing Big Data Important?
Big Data – Big Deal
Scouting Football
• “Moneyball” movie• Evaluate skills relevant to
position– Forward, Mid-fielder,
Goalkeeper
• Predictive team selection
Big Data in Education
Meaningful Insight• Students• Instructors• Programs• Institution
Real-time Data• Dashboards• Statistical
Analysis• Machine
Learning• Data Modeling• Data Mining
Transforming Education• Grades• Outcomes• Evidence• Life-long
Learning
Personalized Learning
So what does this mean for Education?
Big Data – US Higher Ed Problem
Student Retention
• First year attrition rates exceed 25%
• Some states reach 40%
• Only 1 in 2 students ever complete a degree
Degree Completio
n
• 75% students are non- traditional
• 40% students are not academically prepared
• 40% are part time
Time to Degree
• 60% FT students complete 4 yr Bachelor’s within 8 yrs
• 24% PT students complete 4 yr Bachelor’s within 8 yrs
• 20% take more courses than needed
Efficacy of
Higher Ed
Source: Complete College AmericaTime Is the Enemy - Summaryhttp://completecollege.org/docs/Time_Is_the_Enemy_Summary.pdf
Insi
gh
t an
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nfo
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ion
Val
ue
D2L Integrated Learning and Advanced Analytics Platform
Risk Forecasting
Predictive Modeling
What will happen?
Stage Two
ReportingDataAccess
What has happened? What is happening?
Stage One
OptimizationStrategic
What do I want to happen?
Stage Three
Advanced Predictive
Advanced Adaptive
What do you want to happen for you??
Stage Four
ILP - Analytics Capability and Maturity Model
InsightsTM Assessment Reports Module
InsightsTM Achievement Reports Module
Desire2Learn Degree CompassTM
“Our primary motivation for deploying Degree Compass was to respond to the unique success and retention needs of our complex student population.” Dr. Tristan Denley | Austin Peay State University | Higher Ed, Tennessee, US
InsightsTM Student Success System Module
“The Risk Quadrant and Sociogram give us an incredibly different viewpoint on how our students our trending – much more discretely that we can do in our own minds.” Rick Tanski | Academy Online High School | K-12, Colorado, US
Adaptive Learning
• Knowillage LeaP• Adaptive learning engine• Personalized learning experienceWhat if textbooks could learn . . . from you?
Analytics Driving Student Success
Degree Compass™
Student Success
While student is in the course
Before student is even in the course
Text Analytics
• Semantic learning• Data mining analysis• Examples:
– Survey responses– Customer feedback– Journals and publications– Discussion forums– Email threads
Text Analytics
Subtitle
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Michael Moore, MSCISSr. Advisory Consultant - AnalyticsDirect 888.772.0325 x6604Twitter: @[email protected]
Let the dataset change your mindset.