3 EMSIExecutive SummaryJanuary 2011The Economic Contribution of Washington Community and Technical Colleges
4 Economic Growth Analysis: 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 StudentsEconomic Impact Analysis at a GlanceAdded IncomeCollege Operations Effect $746,568,000Student Spending Effect $ 75,869,000Total Spending Effect $822,438,000Student Productivity Effect $10,225,902,000GRAND TOTAL $11,048,339,000
5 $10.2 Billion in State IncomeHigher earnings of students and increased output of businesses
9 Washington Community and Technical Colleges are a Sound InvestmentColleges 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.
10 Total Job Postings in the Health Care Industry, Spokane Region January 2010 – June 2014Due 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.
12 Student Transition Information Project (STIP) “Empowering Community Colleges toBuild the Nation’s Future”41 School Districts73 High SchoolsEnhance the data reporting that guides local and policy-level career and collegereadiness decision making
13 Key Findings Report – Change from 2011 No significant changes in benchmark aggregate scores since 2011 survey
14 Next StepsWe will examine these results in more detail throughout the yearExperiment with the use of CCSSE item responses as predictors of student success:Identify groups of students who may need additional helpMay help target the specific kinds of interventions requiredWe will also examine results of the Community College Faculty Survey of Student Engagement (CCFSSE)Perception-matching between students and faculty
15 Today — CCFSSE:Online survey administered to the same faculty whose classes were selected for the CCSSE sample – 206 instructors district- wide96 items that are matched to student items in CCSSE85-90% are significantly different*We’ll examine items that show some of the greatest difference in perceptions between instructors and studentsDistrict results, not college-specific
16 How students spend their time: Students said they are spending more time preparing for class than faculty believed.11 or more hrs/weekFaculty: 31%Students: 42%¾ of students said they are not participating in extra-curricular activities at all!Faculty: 90% said 1 or more hourStudents: 25% said 1 or more hour
17 Building the model – Operating Philosophy Find and use leading predictors of change along with known enrollment data from current year.
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)
19 Building the model – Validation Model slightly over-estimates upward trend change, and under- estimates downward trend change, but only by 2-3%.
20 Ancillary FindingsRace/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.
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.”
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
23 How will predictive analytics help our students? Help define new student groupsCapacity to predict behaviors from day zeroWhat variables have greatest predictive powerCreate dashboard of student level dataEvaluate existing student success interventions
24 This project has been able to specifically identify points of loss. WICHE Big Data ProjectStudent SuccessThis project has been able to specifically identify points of loss.
25 Quantified Intervention Effectiveness Results Actionable ModelsQuantified Intervention Effectiveness ResultsClosed Loop Field Tests (at-risk)TutoringStudent ServicesText Message AlertsInstitutional BenchmarksCollaborative Community of Experts
26 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?
27 Predict student behaviors Learning outcomesRecruitmentRetentionAim is to make positive changes throughout the student life-cycleIncrease operational efficiencyDemonstrate accountability for accreditationDemonstrate positive efforts to legislature, et al.
28 Not a Silver bulletCannot measure: homesickness, missing girl/boy friend, emotionally unprepared for the freedom of living away from home.
29 Learner analytics Can assignments/ activities be a proxy for engagement?Successful behaviors in a classCourse sequencingRate of student progressFeatures of the learning environment that lead to better learning
30 Learner analytics, cont. Impact of attendanceIndicators of satisfaction and engagementClassroom – virtual or traditionalKeeping the most personal aspects of teaching in place.
31 Challenges Resources: time and people Data cleaning Data formatting andData alignmentChoosing what data to mineInvolve stakeholders early and oftenArticulate clearly how data is collected and how it will be used
32 Challenges, cont. Technologies: interoperability Ability to translate data into actionResources for interventionsPhilosophically - Intrusive approach vs PrivacyRight to Fail
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?
34 Next steps President and Provost are supportive? Capacity to collect and disseminate information?ROI should be quantifiable and clear.
35 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.conclusion