Transcript Document

BIG DATA: OPPORTUNITIES
AND CHALLENGES IN TODAY’S
COMPETITIVE ENVIRONMENT
DR. NANCY SZOFRAN, PROVOST
COMMUNITY COLLEGES OF
SPOKANE
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Highpoint:
10.58 in
1973
Current:
5.57 in
2013
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EMSI
Executive Summary
January 2011
The Economic Contribution of
Washington Community and
Technical Colleges
3
Findings:
Economic Growth Analysis:
• $822.4 million – Income to WA Economy Each Year
• $746.6 million – Operations of 34 Community & Technical Colleges
• $75.9 – Spending of International Students
Economic Impact Analysis at a Glance
Added Income
College Operations Effect
Student Spending Effect
Total Spending Effect
Student Productivity Effect
Total Added Income in
Washington ($ Millions)
$822.40
$746,568,000
$ 75,869,000
$822,438,000
$10,225,902,000
$10,225.90
GRAND TOTAL
Spending
Effect
Productivity
Effect
$11,048,339,000
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2009-2010
 $10.2 Billion in State
Income
 Higher earnings of
students and increased
output of businesses
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Investment vs Future Income
$7.00
$6.00
$5.00
$4.00
$3.00
$5.90
$2.00
$1.00
$0.00
$1.00
For Every $1 Invested
Cumulative in higher income
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Washington benefits from:
• Improved Health
• Reduced Welfare
• Reduced Unemployment
• Reduced Crime
• Savings to the public of $50.7 million per year
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Taxpayer Return on Investment
Comparative Rates of Return
8.00%
7.00%
6.00%
5.00%
4.00%
3.00%
2.00%
1.00%
0.00%
6.40%
7.00%
3.00%
Discount Rate
Washington's
Stocks & Bonds
Community and
Technical Colleges
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Washington Community and
Technical Colleges are a
Sound Investment
 Colleges enrich the lives of
students and increase lifetime income.
 Taxpayers see increased
revenues from an enlarged
economy and a reduction in
the demand for taxpayer
supported social services.
 Colleges contribute to the
vitality of state and local
economics.
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Total Job Postings in the Health Care Industry, Spokane Region
January 2010 – June 2014
3500
3122
3000
2666
2500
2368
2000
1707
1500
1550
1000
806
500
0
2246
2108
1469
1377
1859 1897 1807
2027
1716
2185
2032
1845
Due to the economic growth and
improved data-mining software,
Burning Glass Labor/Insight ™
recognizes 62 percent more total
job postings starting in Q3 2013.
The data from Q3 2013 to Q2 2014
has been normalized to reflect this
change.
0
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Digital Footprints
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Student Transition Information
Project (STIP)
“Empowering Community Colleges to
Build the Nation’s Future”
• 41 School Districts
• 73 High Schools
Enhance the data reporting that guides local and
policy-level career and college
readiness decision making
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80
70
60
50
40
30
20
10
0
Active &
Student Effort
Collaborative
Learning
SCC 2011
No significant
changes in
benchmark
aggregate scores
since 2011
survey
Academic
Challenge
Student-Faculty
Interaction
Support for
Learners
SCC 2014
Standardized Benchmark Score*
Standardized Benchmark Score
KEY FINDINGS REPORT – CHANGE FROM 2011
80
70
60
50
40
30
20
10
0
Active &
Collaborative
Learning
Student Effort
SFCC 2011
Academic
Challenge
Student-Faculty
Interaction
Support for
Learners
SFCC 2014
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NEXT STEPS


We will examine these results in more detail
throughout the year
Experiment with the use of CCSSE item
responses as predictors of student success:



Identify groups of students who may need additional help
May help target the specific kinds of interventions required
We will also examine results of the Community
College Faculty Survey of Student Engagement
(CCFSSE)

Perception-matching between students and faculty
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TODAY — CCFSSE:
 Online survey administered to the same faculty whose classes
were selected for the CCSSE sample – 206 instructors districtwide
 96 items that are matched to student items in CCSSE
 85-90% are significantly different*
 We’ll examine items that show some of the greatest difference
in perceptions between instructors and students
 District results, not college-specific
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HOW STUDENTS SPEND THEIR TIME:
Students said they are
spending more time
preparing for class than
faculty believed.
About how many hours in a typical 7-day week are spent
preparing for class (studying, reading, writing, rehearsing,
doing homework, or other activities related to your
programs)?
Percent Response
50.0
40.0
30.0
11 or more hrs/week
Faculty: 31%
Students: 42%
20.0
10.0
0.0
1 to 5
6 to 10
Faculty
11 to 20
21 to 30
More than
30
Students
¾ of students said they are not
participating in extra-curricular
activities at all!
Faculty: 90% said 1 or more hour
Students: 25% said 1 or more hour
About how many hours in a typical 7-day week are spent
participating in college-sponsored activities (organizations,
campus publications, student government, sports, etc.)?
80.0
Percent Response
None
60.0
40.0
20.0
0.0
None
1 to 5
6 to 10
Faculty
11 to 20
21 to 30 More than
30
Students
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BUILDING THE MODEL – OPERATING PHILOSOPHY
Find and use leading predictors of change along with
known enrollment data from current year.
Winter Enrollment Forecast
Summer
2012-13
Fall
Winter
Spring Summer
2013-14
Fall
Winter
Use half-year enrollment, plus
other summer & fall data
Spring Summer
Run model, late January
2014-15
Fall
Winter
Spring
Annual Enrollment
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BUILDING THE MODEL – BEHAVIORAL INFLUENCES
We examined dozens of potential economic variables.
Variables that panned out:
 Job-related
(Annual employment, Change in annual employment, Net
change in jobs, Unemployment rate)
 Wage-related
(Annual total wages, Change in wages, Average annual
weekly wages)
 Tuition
(State resident tuition, change in annual resident tuition)
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BUILDING THE MODEL – VALIDATION
CCS Enrollment Forecast for 2014-15 -- Winter Models
62000
Student-Quarters
60000
R = 0.94
58000
56000
54000
52000
50000
48000
ACTUAL
FORECAST
Model slightly over-estimates upward trend change, and underestimates downward trend change, but only by 2-3%.
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ANCILLARY FINDINGS
Race/Ethnicity and Financial Aid variables were
overshadowed by other predictors.
Ratio of females to males is predictive for certain
groups – some variables serve as proxies for
things that can’t be directly measured.
Average credit load decreasing  more part-time
students  higher per credit revenue.
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“An area of statistical
analysis that deals with
extracting information
using various technologies
to uncover relationships
and patterns within large
volumes of data that can
be used to predict
behavior and events.”
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Smart Companies: Holistic Approach to Big
Data – Strategies That Enable Solutions
 Predictive Analytics uses data science to
build highly predictive models of future
outcomes.
 Predictions based on student
characteristics and behaviors
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How will predictive analytics help our
students?
 Help define new student groups
 Capacity to predict behaviors from day zero
 What variables have greatest predictive power
 Create dashboard of student level data
 Evaluate existing student success interventions
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WICHE Big Data Project
Student Success
This project has been able to specifically
identify points of loss.
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Actionable Models
Quantified Intervention Effectiveness Results
Closed Loop Field Tests (at-risk)
 Tutoring
 Student Services
 Email
 Text Message Alerts
Institutional Benchmarks
Collaborative Community of Experts
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STUDENT SERVICES QUESTIONS
 Who are our
students?
 What support
services are most
effective and in
what sequence?
 What course
sequencing is
beneficial vs toxic?
 Early alert system:
is the system
actionable,
meaningful?
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PREDICT STUDENT BEHAVIORS
 Learning
outcomes
 Recruitment
 Retention
 Aim is to make
positive changes
throughout the
student life-cycle
 Increase
operational
efficiency
 Demonstrate
accountability for
accreditation
 Demonstrate
positive efforts to
legislature, et al.
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Cannot measure:
homesickness,
missing girl/boy
friend, emotionally
unprepared for the
freedom of living
away from home.
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LEARNER ANALYTICS
 Can assignments/
activities be a proxy
for engagement?
 Successful
behaviors in a class
 Course sequencing
 Rate of student
progress
 Features of the
learning environment
that lead to better
learning
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LEARNER ANALYTICS, CONT.
 Impact of
attendance
 Indicators of
satisfaction and
engagement
 Classroom –
virtual or
traditional
 Keeping the most
personal aspects
of teaching in
place.
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CHALLENGES
 Resources: time
and people
 Data cleaning
 Data formatting
and
Data alignment
 Choosing what data
to mine
 Involve stakeholders
early and often
 Articulate clearly how
data is collected and
how it will be used
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CHALLENGES, CONT.
 Technologies:
interoperability
 Ability to
translate data
into action
 Resources for
interventions
 Philosophically Intrusive
approach vs
Privacy
 Right to Fail
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ARE YOU READY?
 What questions are you trying to answer?
 Will data mining help you answer the
questions?
 Do you have a culture of evidence-driven
decision making?
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NEXT STEPS
 President and Provost are supportive?
 Capacity to collect and disseminate
information?
 ROI should be quantifiable and clear.
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The more data we have
about more people, the
more we can improve
services to individual
students. We can begin to
offer more customized,
personalized choices to
help them meet their
educational goals.
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