Transcript Rise of Big Data in Higher Education

Rise of Big Data
in Higher Education
EDUCAUSE
Webinar
March 22, 2012
By: Louis Soares
Center For American Progress
Overview
•
•
•
•
•
Personal Data and Consumer Agency
Big Data in Higher Education?
Why Big Data Matters?
Co-Creating Value with Big Data
Institutional Practices and Public Policies
What if Education Data was
Personal and Mobile?
http://www.youtube.com/watch?NR=1&v=8O1i0InZ8bM&feature=endscreen
The Rise of Consumer Agency
Big Data In Higher Education?
What is Big Data?
• Fine-grain Information
– Customer Experiences
– Organizational Processes
– Emergent Trends
• Generated By Doing Business
Students Doing Business
•
•
•
•
•
•
•
•
Course Selection
Course Registration
Apply for Financial Aid
Class Participation
Study Alone or in groups
Use Online Resources
Purchase/Return Textbook
Work to support education
Black Box EDU
Technology-Enabled Learning
Each of these interactions
is an opportunity to gather
Big Data
U.S. Department of Education, National Education Technology Strategy, 2010
Questions?
Why Big Data Matters?
• Cost
• Quality
• Knowing the customer
• Value Co-Creation
College Is Expensive
Quality Is In Question
Study of 2,300 undergraduates
– 45 percent “demonstrated no
significant gains in critical thinking,
analytical reasoning, and written
communications during the first two
years of college”
– 36 percent show no improvement in
four years
Additional 16M degrees needed
to be the most educated by 2020
# of Credentials
Source
1.3 million degrees projected population growth
4.3 million degrees increase high school graduation rates, college-going rates of
recent HS graduates, and postsecondary graduation rates
4.2 million degrees half of the 8.4 million adults (25-34) w/ some college complete
degree
2.6 million degrees third of the 8.8 million adults (35-44) w/ some college complete
degree
3.4 million degrees fifteen percent of the 22.7 million adults (25-44) who have
completed high school, but not attended college, complete a
degree
Source: National Center for Higher Education Management Systems, 2009
Know Your Customer
Characteristics on Non-Traditional
•
•
•
•
•
•
•
delayed enrollment PSE beyond the
first year after HS
Attend part time
Are financially independent from their
parents
Work full time
Have dependents other than a spouse
Are a single parent
Have no high school diploma or GED
What Is A Service?
An offering in which:
• “deeds, processes, and
performances” are provided
in “exchange relationships”
among organizations and
individuals
• Value is co-created by
supplier and consumer
• Examples include:
–
–
–
–
educational services,
health care services,
financial services,
Transportation services,
College As A Service
Service Relationship
A. University
A & B create value together
Responsibility Relationship
Responsibility Relationship
A on C
University Resources
People
Technology
Processes
B. Student
C. College Education
Transforms student knowledge
through:
agreements, relationships and
other exchanges
among students and university
faculty, including
courses offered and taken,
tuition paid, and work-study
arrangements.
B on C
Student Resources
Finances
Preparation
Self-Awareness
Informed
Questions?
Co-Creating Value with Big Data
Student Learning
•
•
•
•
•
425,000 students
Web-based learning environments
Self-directed Learning
Adaptive instructional software
Data Dashboards
– Improve individual performance
– Enhance course redesign
– Predict future performance
Course Enrollment
• 40,000 Students
• Course Recommendation Engine
– Service Oriented Higher Education
Recommendation Personalization
Assistant
• Student Profile
– Course preferences
– Schedules
– Past courses
• Tools
– Tutors
– Time-management tools
– Life-planning resources
SHERPA
Course Success
•
•
•
•
Early Warning System
Study patterns and performance
Student/Faculty Dashboard
Profile Development
– Student demographics
– Grade books
– Activity logs from online resources
• Benchmark successful students
• Seek Support
Student Lifestyle Management
• Learning Communities
• Behavioral Science
• Student Profile
– Work/life details
– Academics
– Preferences
• Nudges to stay on-track
–
–
–
–
Mobile Platform
Time management
Academic Setbacks
Peer groups
Institutional Practices
and
Public Policies
Five Practices of High Performing Institutions
Increase Rate of Degree Completion
• Culture of Completion and
Outplacement
• Reduce nonproductive credits
Reduce Cost per Student
• Redesign instruction delivery
• Redesign core support services
–
•
(HR, IT, Finance, student services,
academic support services, plant
operations)
Optimize non-core services and
other operations
–
(research, public services, auxiliary
enterprises)
Six Characteristics of Instruction Redesign that
Improve Completion and Reduces Costs
Whole Course
Redesign
Target whole course not a single class
Analyze time spent on each activity in course by person
Active Learning
Move course from teacher led to active and learner-centered
Note taking replaced by active learning exercises
Computer-based
Learning
Web-based tutorials and exercises and
low-stakes quizzes frequent practice and feedback
Mastery
Learning
Greater flexibility for when students can engage with a course, not self-paced
Organized by the need to master specific learning objectives, modular
On-Demand
Help
Variety of different supports build sense of learning community
Projects replace lectures w/ small group activities (tech supported, staffed
assisted)
Alternative
Staffing
Apply right level of human intervention to particular problems
Task specific labor: faculty v. GTA, Peer mentors, Course assistant
IT Infrastructure for Big Data
Source: Action Analytics, EDUCAUSE REVIEW,January/February 2008, Authors: Donald Norris, Linda Baer, Joan Leonard, Louis Pugliese, and Paul Lefrere
Public Policies for Big Data
1. Create guidelines for how data generated through these
technology tools should be treated in order to promote student
privacy while allowing for the data to be shared in a social
environment.
2. Review the data it currently collects to find areas where the
information might supplement the emerging user-generated
data in ways that help students make better choices.
3. Fund the development or spread of emerging “personalization”
tools through competitive grants. A special focus could be
placed on institutions that serve low-income students and
students of color.
THANK YOU!
QUESTIONS??
DISCUSSION