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BIG DATA: OPPORTUNITIES AND CHALLENGES IN TODAY’S COMPETITIVE ENVIRONMENT DR. NANCY SZOFRAN, PROVOST COMMUNITY COLLEGES OF SPOKANE DR. NANCY SZOFRAN, PROVOST.

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Presentation on theme: "BIG DATA: OPPORTUNITIES AND CHALLENGES IN TODAY’S COMPETITIVE ENVIRONMENT DR. NANCY SZOFRAN, PROVOST COMMUNITY COLLEGES OF SPOKANE DR. NANCY SZOFRAN, PROVOST."— Presentation transcript:

1 BIG DATA: OPPORTUNITIES AND CHALLENGES IN TODAY’S COMPETITIVE ENVIRONMENT DR. NANCY SZOFRAN, PROVOST COMMUNITY COLLEGES OF SPOKANE DR. NANCY SZOFRAN, PROVOST COMMUNITY COLLEGES OF SPOKANE 1

2 Highpoint: in 1973 Current: 5.57 in

3 EMSI Executive Summary January 2011 EMSI Executive Summary January 2011 The Economic Contribution of Washington Community and Technical Colleges 3

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

5  $10.2 Billion in State Income  Higher earnings of students and increased output of businesses  $10.2 Billion in State Income  Higher earnings of students and increased output of businesses 5

6 6

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

8 Taxpayer Return on Investment 8

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

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 Q The data from Q to Q has been normalized to reflect this change. 10

11 Digital Footprints 11

12 Student Transition Information Project (STIP) “Empowering Community Colleges to Build the Nation’s Future” 41 School Districts 73 High Schools 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 Enhance the data reporting that guides local and policy-level career and college readiness decision making 12

13 KEY FINDINGS REPORT – CHANGE FROM 2011 No significant changes in benchmark aggregate scores since 2011 survey 13

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

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*  We’ll 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*  We’ll examine items that show some of the greatest difference in perceptions between instructors and students  District results, not college-specific 15

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% 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 16

17 BUILDING THE MODEL – OPERATING PHILOSOPHY Find and use leading predictors of change along with known enrollment data from current year. 17

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

19 BUILDING THE MODEL – VALIDATION Model slightly over-estimates upward trend change, and under- estimates downward trend change, but only by 2-3%. 19

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 can’t 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 can’t be directly measured.  Average credit load decreasing  more part-time students  higher per credit revenue. 20

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

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

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

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

25  Actionable Models  Quantified Intervention Effectiveness Results  Closed Loop Field Tests (at-risk)  Tutoring  Student Services   Text Message Alerts  Institutional Benchmarks  Collaborative Community of Experts 25

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

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

28 NOT A SILVER BULLET Cannot measure: homesickness, missing girl/boy friend, emotionally unprepared for the freedom of living away from home. 28

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

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.

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

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.

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

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

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. 35


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