Innovative DataDriven Decision
Making
James Frazee
San Diego State University
John Fritz
University of Maryland Baltimore County
Ellen Wagner
WCET
John Whitmer
Blackboard
Outline
Introductions & Brief Overviews
Context: Big Data Aspirations
& Limited Implementations
Panel Discussion
Audience Discussion
Bios
John Fritz
James Frazee
Ellen Wagner
Assistant Vice President
for Instructional Technology
in UMBC's Division
of Information Technology
Senior Academic Technology
Officer and Director
of Instructional Technology
Services (ITS) at San Diego
State University
Chief research and strategy
officer and co-founder
of the PAR (Predictive Analytics
Reporting) Framework
Introductions
& Brief Overviews
Guiding Question
What are the most
important things that
you’ve learned so far?
The Predictive Analytics Reporting
(PAR) Framework
PAR is a “massive
data” analysis effort
using predictive
analytics
to identify drivers
related to loss
and momentum
and to inform
student loss
prevention efforts
in US postsecondary
education
PAR institutional
members voluntarily
contribute
de-identified student
records to create
a single federated
database built
around open
published data
definitions, using
data tools already in
use in .edu
Results of
descriptive,
inferential
and predictive
analyses create
benchmarks,
localized institutional
predictive models
and tools to
inventory, map and
measure student
success
interventions
On track
to becoming
an independent
501(c)(3) provider
of learner analytics
as a service
by the end of 2014
PAR Framework per the Institute for Higher
Education Policy (http://www.ihep.org/)
SP2014 Course Accesses
Grades
Grade Center as Proxy
for Design
Learning Analytics Pilot
Interventions
# Students Receiving >0 Interventions:
PSY: 177 (84%) STAT: 165 (70%)
Significant Effect for PellEligible Students in PSY 101
100%
90%
80%
70%
Relationship: Receive Interventions & Course Pass
23%
(Pell-Eligible vs. Overall Students)
PSY: Pearson chi2(1) = 6.4007 Pr = 0.011
9%
91%
77%
18%
83%
14%
87%
60%
50%
Not Pass
40%
Pass
30%
20%
10%
0%
No Interventions
(PSY, Pell-Eligible)
Interventions
(PSY, Pell-eligible)
No Interventions
(Overall)
Interventions
(Overall)
Context:
Aspirations
& Limited
Implementations
Source: Educause and AIR, 2012 (2012), http://goo.gl/337mA
Source: Educause and AIR, 2012 (2012), http://goo.gl/337mA
Source: Educause and AIR, 2012 (2012), http://goo.gl/337mA
Source: McKinsey: “Big Data” Report (2011), http://goo.gl/hdwyy
Source: McKinsey: “Big Data” Report (2011), http://goo.gl/hdwyy
Source: McKinsey: “Big Data” Report (2011), http://goo.gl/hdwyy
Panel
Discussion
Q1
What is the most exciting thing
you’re working on right now?
Q2
What has been the impact of analytics
at your campus?
Improvements in learning and teaching?
Improvements to business
or organizational processes?
Q3
Are you seeing changes in leadership and
organizational factors that will contribute
to an increased rate of analytics adoption?
Q4
What emerging technologies
(or standards) will contribute to an
increased rate of analytics adoption?
What current problems are being solved?
Q5
What current problems are not being
addressed by emerging technologies
(or standards) that we should be paying
attention to?
Q6
Words of wisdom?
What do you know now that you wish
you had known when you got started?
Parting Note
* Used with permission by my daughter, Zoe Fritz. 
Discussion
fritz@umbc.edu
james.frazee@mail.sdsu.edu
edwsonoma@gmail.com
john.whitmer@blackboard.com
@johncwhitmer
Resources
Learning Analytics Listserv
learninganalytics+subscribe@googlegroups.com
SOLAR - Society for Learning and Knowledge Analytics
http://solaresearch.org/
Predictive Analytics Reporting Project
http://wcet.wiche.edu/par
Blackboard Analytics
http://www.blackboard.com/analytics
More resources
http://bit.ly/la_resources