BIG DATA: OPPORTUNITIES AND CHALLENGES IN TODAYS COMPETITIVE
ENVIRONMENT DR. NANCY SZOFRAN, PROVOST COMMUNITY COLLEGES OF
SPOKANE DR. NANCY SZOFRAN, PROVOST COMMUNITY COLLEGES OF SPOKANE 1
Slide 2 Highpoint: 10.58 in 1973 Current: 5.57 in 2013 2 Slide 3
EMSI Executive Summary January 2011 EMSI Executive Summary January
2011 The Economic Contribution of Washington Community and
Technical Colleges 3 Slide 4 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 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$746,568,000 Student Spending
Effect$ 75,869,000 Total Spending Effect$822,438,000 Student
Productivity Effect$10,225,902,000 GRAND TOTAL$11,048,339,000
Economic Impact Analysis at a Glance Added Income College
Operations Effect$746,568,000 Student Spending Effect$ 75,869,000
Total Spending Effect$822,438,000 Student Productivity
Effect$10,225,902,000 GRAND TOTAL$11,048,339,000 4 Slide 5
2009-2010 $10.2 Billion in State Income Higher earnings of students
and increased output of businesses 2009-2010 $10.2 Billion in State
Income Higher earnings of students and increased output of
businesses 5 Slide 6 6 Slide 7 Washington benefits from: Improved
Health Reduced Welfare Reduced Unemployment Reduced Crime Savings
to the public of $50.7 million per year Washington benefits from:
Improved Health Reduced Welfare Reduced Unemployment Reduced Crime
Savings to the public of $50.7 million per year 7 Slide 8 Taxpayer
Return on Investment 8 Slide 9 Washington Community and Technical
Colleges are a Sound Investment Colleges enrich the lives of
students and increase life- time 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. Washington Community and
Technical Colleges are a Sound Investment Colleges enrich the lives
of students and increase life- time 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. 9 Slide 10 Total Job
Postings in the Health Care Industry, Spokane Region January 2010
June 2014 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. 10 Slide 11
Digital Footprints 11 Slide 12 Student Transition Information
Project (STIP) Empowering Community Colleges to Build the Nations
Future 41 School Districts 73 High Schools Student Transition
Information Project (STIP) Empowering Community Colleges to Build
the Nations Future 41 School Districts 73 High Schools Enhance the
data reporting that guides local and policy-level career and
college readiness decision making Enhance the data reporting that
guides local and policy-level career and college readiness decision
making 12 Slide 13 KEY FINDINGS REPORT CHANGE FROM 2011 No
significant changes in benchmark aggregate scores since 2011 survey
13 Slide 14 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 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 14 Slide 15 TODAY CCFSSE: Online survey administered to the
same faculty whose classes were selected for the CCSSE sample 206
instructors district- wide 96 items that are matched to student
items in CCSSE 85-90% are significantly different* Well examine
items that show some of the greatest difference in perceptions
between instructors and students District results, not
college-specific Online survey administered to the same faculty
whose classes were selected for the CCSSE sample 206 instructors
district- wide 96 items that are matched to student items in CCSSE
85-90% are significantly different* Well examine items that show
some of the greatest difference in perceptions between instructors
and students District results, not college-specific 15 Slide 16 HOW
STUDENTS SPEND THEIR TIME: Students said they are spending more
time preparing for class than faculty believed. 11 or more hrs/week
Faculty:31% Students:42% 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 16 Slide 17
BUILDING THE MODEL OPERATING PHILOSOPHY Find and use leading
predictors of change along with known enrollment data from current
year. 17 Slide 18 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)
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)
18 Slide 19 BUILDING THE MODEL VALIDATION Model slightly
over-estimates upward trend change, and under- estimates downward
trend change, but only by 2-3%. 19 Slide 20 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 cant
be directly measured. Average credit load decreasing more part-time
students higher per credit revenue. 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 cant be directly measured. Average
credit load decreasing more part-time students higher per credit
revenue. 20 Slide 21 PREDICTIVE ANALYTICS 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. 21
Slide 22 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 22 Slide 23 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 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 23 Slide 24
WICHE Big Data Project Student Success This project has been able
to specifically identify points of loss. WICHE Big Data Project
Student Success This project has been able to specifically identify
points of loss. 24 Slide 25 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 25
Slide 26 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? What course sequencing is beneficial vs toxic? Early
alert system: is the system actionable, meaningful? 26 STUDENT
SERVICES QUESTIONS Slide 27 Learning outcomes Recruitment Retention
Aim is to make positive changes throughout the student life-cycle
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. Increase operational
efficiency Demonstrate accountability for accreditation Demonstrate
positive efforts to legislature, et al. 27 PREDICT STUDENT
BEHAVIORS Slide 28 NOT A SILVER BULLET Cannot measure:
homesickness, missing girl/boy friend, emotionally unprepared for
the freedom of living away from home. 28 Slide 29 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 29 LEARNER
ANALYTICS Slide 30 Impact of attendance Indicators of satisfaction
and engagement Classroom virtual or traditional Keeping the most
personal aspects of teaching in place. 30 LEARNER ANALYTICS, CONT.
Slide 31 Resources: time and people Data cleaning Data formatting
and Data alignment 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 Choosing what data to mine
Involve stakeholders early and often Articulate clearly how data is
collected and how it will be used 31 CHALLENGES Slide 32
Technologies: interoperability Ability to translate data into
action Resources for interventions Technologies: interoperability
Ability to translate data into action Resources for interventions
Philosophically - Intrusive approach vs Privacy Right to Fail
Philosophically - Intrusive approach vs Privacy Right to Fail 32
CHALLENGES, CONT. Slide 33 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? 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? 33 Slide 34 NEXT STEPS President and Provost are
supportive? Capacity to collect and disseminate information? ROI
should be quantifiable and clear. President and Provost are
supportive? Capacity to collect and disseminate information? ROI
should be quantifiable and clear. 34 Slide 35 CONCLUSION 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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