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Building a Continuing EducationEnrollment & Retention Modelfrom ScratchMatthew HendricksonAssociate Director – Strategic E...
Contents• NEU & CPS• Need - Higher Ed• Need – CPS• Building the Model• Predicting the Future• What We Learned• Next Steps
• 8 Colleges• 30,000 Students– 169 Graduate & 94Undergraduate programs• Professional & continuingeducation from 1898• Dist...
Need – Higher Education• need for training• Lack of “non-traditional” models• government $ support
Lack of models• Current expectations for Continuing Ed– Required reporting– Tracking capacity– Net revenue & enrollments• ...
Tinto (1975)Tinto, V. (1975). Dropout from Higher Education: A Theoretical Synthesis of Recent Research. Review of Educati...
Look familiar?• “Fit”• 1st year programs• HS GPA• SAT/ACT• Proximity to home• Financial situation• First generation
What’s the problem?• Returning years later• Years to degree• Transfer / Swirling• Drop- & stop-outs• No SAT/ACT• Employed•...
Need - CPS• Understand & stabilize enrollment• Prioritize recruitment & applications• Determine predictors for success• Pr...
Building the model• Canvas available data• Best tracking method• Handling drop-/stop-outs
Data problems / challenges• Systems– Legacy 2003 - 2009– Banner 2009 - current• Inconsistent coding & collection• Cohort i...
How did we determine the cohorts?• Not traditional Fall – Fall enrollment cycle• Inconsistent enrollments & entries• Finan...
Definitions• Cohort• Returning Student• Graduating Student
Other cohort options?• Start and complete a single term• Term to term• Individual tracking
Combined report (masked data)Sample Bachelors DegreesProgram Returning & Graduating RatesInitial Year 2nd Year 3rd YearCoh...
Importance• Significant 1st step• Understanding of student population• Confirm hunches• Visualize enrollment behavior• Ben...
Predicting future enrollments• Two part:– Averages• 3 Years– Rolling– Weighted– Statistical modeling• Regression analysis
What have we learned?• Improving numbers• Increased data consistency• New data element collections• Internal benchmarks
How will we use it?• Enrollment Management• Strategic Planning• Budgeting• Program Evaluation
Immediate next steps• Recruitment prioritization• Application questions• Identify data issues• Create longitudinal dataset...
What’s next - Long term goals• Use available data for retention modeling• Identify predictors• Continue thought and indust...
Questions & Thank You• Contact info:– m.hendrickson@neu.edu– 617.373.8175• LinkedIn– http://www.linkedin.com/in/matthewjhe...
AIR Presentation InfoTitle: Building a Continuing Education Enrollment & Retention Model from ScratchTrack: Students: Enro...
AIR 2012 Enrollment & Retention 06.05.2012
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AIR 2012 Enrollment & Retention 06.05.2012

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Presented at the 2012 Association for Institutional Research Forum in New Orleans, LA.

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  • Here are the topics that will be coveredBrief description of NEU & CPSNeed – Higher Ed in generalLack of models – although some are being createdNeed – CPSBuilding the modelData Issues – Problems & InconsistenciesSample reportPredicting the futureWhat we have learnedNext steps
  • NEU is an institution on the moveRise in rankingsPushing the envelope of distance educationCampus expansionCharlotte online / Seattle operational later this yearCPSHoused within NEUSupports regional campusesGoal to educate non-traditional studentsPractitioner/applied programsFlexible to meet market demands
  • Increased need for training:President’s 2020 Completion goalUnemploymentChange of careersLack of models other than traditional modelsUniqueness of non-traditional institutions makes creating a model difficultDecreased governmental monetary supportCuts to Pell fundingSpending consolidations in Higher Ed (national)With less money, what services can we offer to help our students attain degrees?http://www.whitehouse.gov/omb/budgethttp://www.whitehouse.gov/sites/default/files/omb/budget/fy2013/assets/budget.pdfhttp://www.whitehouse.gov/sites/default/files/omb/budget/fy2013/assets/education.pdfPell p.106 // Higher Ed p.132
  • Current expectations:Federal reporting often isn’t required beyond federalfinaidreq’sEx: IPEDS – Less if attached to major university – lumped with “main campus” // EthicsSimply don’t have the capacity to track the studentsExplosive growth in online & continuing educationNo physical rooms – capacity increased with enrollmentsStrained staffStill exploring best-practices for online & adult learnersFocus on revenue generation and sheer enrollments“Greed” of institutions – padding of budgets, etc.Increased retention would help with increased revenuesThe ability to help students succeed is tantamounthttp://www.jmhconsulting.com/continuing-education-retention-analyticsAvailable: List of links from Kansas State Universityhttp://www.nacada.ksu.edu/clearinghouse/advisingissues/retain.htmFed & State pressures: reporting, accountability, & learning outcomes
  • Can’t talk about retention without mentioning TintoIf not familiar – tons of ink has been spilled on this topicYet this model is difficult to translate to adult & continuing education, particularly:Pre-College schooling – no longer relevant?How long since these students have taken classes?Peer Group Interactions – most have families and work full time and have a lessened need for peer interactionsOnline students may lack these interactionsSome ability for interaction – web chat, etc.Social IntegrationStudents can complete a degree without ever meeting other students
  • I am sure you have all seen theseTypical traditional student success concepts“Fit”First year programmingHS GPASAT/ACTProximity of home to campusFinancial situationFirst generationWhat do you do when your student base doesn’t represent the typical traditional student?
  • Many have been out of school for yearsSheer time to degree1-2 courses a term = 10+ years for Bachelor’s degreeStudents in and out of the pipeline (swirling)Accounting for this flow can be difficult with current data systemsHow do you accept transfer credits?How do you account for dual enrollments?Differentiating between drop & stop outsWhen do you consider them “out” of the systemCurrent CPS policy is 4 terms of inactivity (1 year)Many don’t have standardized test scores, or they aren’t representativeMany of our students are employed full timeOften looking for a career change, so must start from the beginningCharacteristics of students in non-traditional degree paths
  • Growing pains - entrepreneurial spirit - grew rapidlyUnderstanding what happened // Create benchmarks // Program effectivenessStabilize enrollmentCPS had trouble managing the influx (student support, tracking, etc.)Create a comprehensive retention strategy for enrolled studentsPrioritize recruitment & applicationsWhile remaining accessible (core value) http://www.cps.neu.edu/discover/mission-vision-core-values.phpDetermine predictors for successIsolate inputs that indicate success and failureUse this in the application processEnsure programming & current student assistance improvesThis is the main focus – “Accessible”Not just seeking “the best” students, but helping those wishing to enroll & complete their degreesHow can we support current & future students?Policies and interventions to remove barriers to success
  • Canvas available data:Understand data limitationsInconsistent codingChangeover of data systemsBest method for tracking:Define cohorts (will do shortly)Degree seeking only – because of degree completion programmingNot including special students (longitudinal data issue)Special students = those taking classes who aren’t registered as degree seekingHandling of drop-/stop-outs
  • Changeover of data systems in 2009Inconsistent coding between systems and within current systemLack of data – some is simply not collectedNo easy way to identify cohort – no indicatorsHad to merge data tables and force through numerous filters & logicTough to determine a first time degree seeking studentMany were excluded in the early data reports due to lacking data
  • Students in continuing & online education often don’t follow a fall-fall enrollment cycle like traditional higher edNot the first-time full-time enrollmentsOften enroll, stop, return, etc. and can enter at multiple points (4x) during a yearSo how do we define a cohort? (next slide…)
  • Everyone has their definition of retention & graduation ratesCohort:Fiscal Year to Fiscal Year based on year of entranceAny new degree seeking student beginning in any term during one Fiscal YearExcludes non-degree seeking students since degree seeking students selected for strategic purposesReturning:Any student beginning in a previous cohort taking a course at any point during a consecutive FY is considered returningStudents may be counted in a cohort, not return for 2nd FY, return for 3rd FY and not be “returning” in the 2nd but are “returning” in the 3rdThis gets at the issue of stop-/drop-outsGraduation Rate:Any student completing a degree within a consecutive FYStudents graduating during that FY are counted as graduating, not returning
  • Cohort definition to date is the first attemptThis is open to refinement & additions at a later timeThe plan is to add models and views rather than to replace this viewThis view is a starting point and has given a great deal of strategic assistanceCurrently useful as it aligns with the fiscal year – useful for budget planningOther possibilities:Term to term trackingTracking at the specific student level
  • Bachelor’s Degree seeking (NOT REAL DATA)This table is a short look (full extends to 7 years – the end of our longitudinal data)Looks at the first 3 years of the report for illustration purposesFull version has counts corresponding to the percentagesExample: FY08Start with 50065% returned for 2nd year while 5% graduated = 70% returned or graduatedCan sum return and graduated because they are not double counted40% returned for 3rd year while 20% graduated = 60% returned or graduated (rounding)Interesting – inverse are those who are unaccounted for
  • First StepThis is the first step of a long processUnderstanding student populationData show the trends of the student base of CPSReference and shows what has happened to dateConfirm hunches of student dataHad no data to support this in the pastVisualize student enrollment behaviorLongitudinal data provide an understanding of the enrollment pipelineGraduation and returning rates are increasing overallBenchmarkTrack significant changes after new projects, programs, or initiatives are put in placeCurrent data are great, but a reference and benchmark are key
  • Two prong approach:1. Create simple formulas in the excel report to give a rough estimation of future enrollmentCurrently in this approachRolling averages, weighted averagesGenerally 3 years since these are the classes that resemble most closely our current enrollments2. Statistical modeling:This is our idea approachRegression analysisPredictors of successSeeking early warning signs
  • Rates climbing:Introduction of degree completion programsAlso a different student baseData consistencySee gaps in data collection & inconsistenciesKnow where to reinforce this infoNew data elementsFinding gaps in analysis and items we would like to addTalks are occurring with admissions for better coding and entryBenchmarkingLook back for comparisons as discussed before
  • Enrollment management purposes:Track studentsUnderstand student flowStrategic planningPredict future enrollmentsBudgetingHelp set budget expectationsProvide better and (ultimately) faster budget predictionsTake out SOME of the guessworkProgram evaluationWhich programs are going strongWhich programs could use some attentionAre we offering what is needed to help our students complete their degree in a reasonable time
  • Goal to increase returning and graduation rates – educating studentsTargeted recruitment strategies – determine locations and institutionsApplication process:Recommend additional data elements to be collectedCodify data input (what is available) & new collection itemsCreate processes to identify students for admission without decreasing qualityKeep access and create higher qualityImplement policies & proceduresKeep it easy to re-enroll for stop-outs, but find better ways of monitoring & entering dataIncrease student support offeringsTrue understanding of how students flow in and out of enrollment
  • Data doesn’t exist in a usable form for retention analysis – if it exists at allData consistency & not measured the same waysNo true model (that is shared) as a standard among higher education institutionsSo we need to create our ownIdentify predictorsDetermine risks specific to CPS studentsCreate and early alert system and/or contact programContinue thought and industry leadershipNon-traditional, adult, and online higher educationLack of data and models for this populationPresent findings at national higher education conferences
  • Thank you for having me come and share this study with youAny questions, comments, or feedback to help with this study are welcome
  • Transcript of "AIR 2012 Enrollment & Retention 06.05.2012"

    1. 1. Building a Continuing EducationEnrollment & Retention Modelfrom ScratchMatthew HendricksonAssociate Director – Strategic Enrollment ResearchJune 5, 2012Session ID: 2592
    2. 2. Contents• NEU & CPS• Need - Higher Ed• Need – CPS• Building the Model• Predicting the Future• What We Learned• Next Steps
    3. 3. • 8 Colleges• 30,000 Students– 169 Graduate & 94Undergraduate programs• Professional & continuingeducation from 1898• Distance educationinnovator (1974)• Regional CampusExpansion• Associate, Bachelor,Master, & Doctoraldegrees• 10,000 Students– 87 Programs– 67 Offered online• Practitioner-basedprograms• Degree completion,pathways, & partnerships
    4. 4. Need – Higher Education• need for training• Lack of “non-traditional” models• government $ support
    5. 5. Lack of models• Current expectations for Continuing Ed– Required reporting– Tracking capacity– Net revenue & enrollments• What is available:– Beginning to focus on models– Federal & State pressures
    6. 6. Tinto (1975)Tinto, V. (1975). Dropout from Higher Education: A Theoretical Synthesis of Recent Research. Review of EducationalResearch, 45(1).
    7. 7. Look familiar?• “Fit”• 1st year programs• HS GPA• SAT/ACT• Proximity to home• Financial situation• First generation
    8. 8. What’s the problem?• Returning years later• Years to degree• Transfer / Swirling• Drop- & stop-outs• No SAT/ACT• Employed• Career change• Changingdemographics
    9. 9. Need - CPS• Understand & stabilize enrollment• Prioritize recruitment & applications• Determine predictors for success• Programming & current student assistance
    10. 10. Building the model• Canvas available data• Best tracking method• Handling drop-/stop-outs
    11. 11. Data problems / challenges• Systems– Legacy 2003 - 2009– Banner 2009 - current• Inconsistent coding & collection• Cohort identification challenges– Multiple field limitations to approximate cohorts
    12. 12. How did we determine the cohorts?• Not traditional Fall – Fall enrollment cycle• Inconsistent enrollments & entries• Financial and budget planning
    13. 13. Definitions• Cohort• Returning Student• Graduating Student
    14. 14. Other cohort options?• Start and complete a single term• Term to term• Individual tracking
    15. 15. Combined report (masked data)Sample Bachelors DegreesProgram Returning & Graduating RatesInitial Year 2nd Year 3rd YearCohort Starting Size Returned %Total %GraduatingTotal %Returning orGraduatingReturned %Total %GraduatingTotal %Returning orGraduatingFY05 150 50% 5% 55% 30% 5% 35%FY06 200 55% 5% 60% 35% 10% 45%FY07 300 60% 5% 65% 40% 15% 55%FY08 500 65% 5% 70% 40% 20% 60%FY09 550 70% 5% 75% 45% 20% 65%FY10 * 850 65% 10% 75%FY11 800
    16. 16. Importance• Significant 1st step• Understanding of student population• Confirm hunches• Visualize enrollment behavior• Benchmark
    17. 17. Predicting future enrollments• Two part:– Averages• 3 Years– Rolling– Weighted– Statistical modeling• Regression analysis
    18. 18. What have we learned?• Improving numbers• Increased data consistency• New data element collections• Internal benchmarks
    19. 19. How will we use it?• Enrollment Management• Strategic Planning• Budgeting• Program Evaluation
    20. 20. Immediate next steps• Recruitment prioritization• Application questions• Identify data issues• Create longitudinal datasets• Implement policies and procedures
    21. 21. What’s next - Long term goals• Use available data for retention modeling• Identify predictors• Continue thought and industry leadership
    22. 22. Questions & Thank You• Contact info:– m.hendrickson@neu.edu– 617.373.8175• LinkedIn– http://www.linkedin.com/in/matthewjhendrickson
    23. 23. AIR Presentation InfoTitle: Building a Continuing Education Enrollment & Retention Model from ScratchTrack: Students: Enrollment and ExperienceFormat: Building IR Capacity: IR in Practice (40-minutes)Presenter: Matthew Hendrickson, Northeastern UniversityAbstract: Due to a lack of retention and enrollment models for continuingeducation students, a new institutional model is created. Student counts andregistrations are combined from multiple data systems, determining “cohorts”of new students by fiscal year that are tracked throughout their enrollmentlifespan. Predictions for future enrollment and expected retention are madeand revised regularly. This information serves not only for enrollmentpurposes, but also budgetary and strategic planning purposes. Participants gaininsight into the entire enrollment and retention projection process from thestart through implementation and first revision. Questions about the processwill be answered.

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