Leveraging Analytics to Improve Student

Success

Karen Vignare, University Maryland University College

@kvignareEllen Wagner, PAR Framework

@edwsonoma

Session Description

• This session shows how analytics can be used to identify opportunities for improving student success.

• By the end of the session, participants will make connections between predictions about risk, and the interventions most likely to work best under varying conditions and with different populations.

Setting the Context: Data Are Changing Everything

“But education researchers have always worked with data.”

• We do qualitative research with data• We do quantitative research with data• We do evaluations with data• We develop surveys and instruments and experiments to

collect more data• We pull data from LMSs, SISs, ERPs, CRMs …• We write reports, summaries, make presentations, develop

articles and books and webcasts….

From Hindsight to Foresight

6

Analytics in Higher EducationLearning Analytics

Best way to teach and learn

Learner Analytics

Best way to support students

Organizational AnalyticsBest ways to operate a college

Academic Analytics

Create new insights and opportunities for data in our practices

• Enrollment management• Student services• Program and learning experience design• Content creation• Retention, completion• Gainful employment• Institutional Culture

9

How Are We Doing So Far?• Data is the number 1 challenge in the adoption and use of

analytics. Organizations continue to struggle with data accuracy, consistency, access.

• The primary focus of analytics focuses on reducing costs, improving the bottom line, managing risk.

• Intuition, based on experience, is still the driving factor in data-driven decision-making. Analytics are used as a part of the process.

• Many organizations lack the proper analytical talent. Organizations that struggle with making good use of analytics often don’t know how to apply the results.

• Culture plays a critical role in the effective use of data analytics.

GROUP DISCUSSION

• Is your institution using (or planning to use) academic analytics specifically to improve student success?

• What kinds of questions are you trying to answer?• What kinds of data are you planning to use? • What kinds of barriers are you encountering?

Getting to the right answer takes work

• Analysis and model building is an iterative process

• Around 70-80% efforts are spent on data exploration and understanding.

SAS Analysis/Modeling Process

Link Predictions to Action

• Predictive analytics refer to a wide varieties of methodologies. There is no single “best” way of doing predictive analytics. You need to know what you are looking for.

• Simply knowing who is at risk is simply not enough. Predictions have value when they are tied to what you can do about it.

• Linking behavioral predictions of risk with interventions at the best points of fit offers a powerful strategy for increasing rates of student retention, academic progress and completion.

Collaborative

National

Multi-institutional Non-profit

Institutional Effectiveness +

Student Success

What PAR doesPAR uses descriptive, inferential and predictive analyses to create benchmarks, institutional predictive models and to inventory, map and measure student success interventions that have direct positive impact on behaviors correlated with success.

Linking Predictions to Action

• Identify obstacles and remove barriers from student success pathways.

• Provide actionable information so students and advisors can build informed opportunity pathways.

• Know where to invest in student success leveraging collaborative insight that determine return on investment in interventions and support.

Benchmarks & Insight Predictive Analytics Intervention Inventory and ROI Tools

Diagnostics

PAR analytic toolset

Benchmarks & Insight Predictive Analytics Intervention Inventory and ROI Tools

Web Tools

Student Success Matrix (SSMx)

PAR by the Numbers

• 2.2 million students and 24.5 million courses in the PAR data warehouse, in a single federated data set, using common data definitions.

• 48 institutions, 351 unique campuses.• 77 discrete variables are available for each student record in the data set.

Additional 2 dozen constructed variables used to explore specific dimensions and promising patterns of risk and retention.

• 343 discrete interventions filtered on predictor behaviors, point in student life cycle, student attributes, institutional priorities and ROI factors in the growing SSMx dataset.

Structured, Readily Available Data• Common data definitions

= reusable predictive models and meaningful comparisons.

• Openly published via a cc license @ https://public.datacookbook.com/public/institutions/par

Speak the same

language

PAR Puts it All Together

Determine students

probability of failure

(predictions)

Determine which students respond to interventions

(uplift modeling)

Determine which interventions are

most effective (explanatory

modeling)

Allocate resources

accordingly (cost benefit

analysis)

Findings from aggregated dataset Positive Predictors

High school GPA (when available)

Dual Enrollment – HS/College

Any prior credit

CC GPA

Credit Ratio

Successful Course Completion

Positive completion of DevEd Courses

Negative Predictors

Withdrawals

Low # of credits attempted

Varies but can be significant

PELL Grant Recipient

Taken Dev Ed

Age

Fully online student

Race

• Measurement resources are usually located separately from intervention planning & implementation resources

• Lack of connection of predictors to interventions and interventions to outcomes

©PAR Framework 2015

Common Challenges for Intervention Effectiveness

PAR Student Success Matrix (SSMx)• An organizational structure that helps institutions

inventory, organize and conceptualize interventions aimed at improving student outcomes.

• A common framework for classifying interventions• Provides a basis for intervention measurement

©PAR Framework 2015

SMALL GROUP DISCUSSIONHow Are You Measuring

Interventions at YOUR Institution?

Specific Examples of Data Driven Improvements

• UMUC / U of Hawaii – replication of community college success prediction studies

• U of Hawaii – “Obstacle courses”• University of North Dakota – predictives tied to student

watchlist data• Intervention measurement at Sinclair CC and Lone Star CC• National online learning impact study on student retention (in

press, based on results from >500,000 students taking onground, blended and online courses)

Intervention Measurement – Student Success Courses Results

• 12 month credit ratio: Only 1 of the 8 Student Success Courses analyzed showed a statistically significant positive effect for students taking the course vs. those who did not.

• Retention: 7 of the 8 courses showed a significantly positive effect

• Retention higher by 14% to 4X

Intervention Measurement – Student Success Courses

Course Component Summary:

Public university offering online degree programs to a diverse population of working adults Largest open access public online university in U.S.Premier provider of higher education to U.S. military since 1949Part of the University System of Maryland

About UMUC

20th CenturyHistoricalLongitudinalWarehouseSiloed ExternalReporting

21st CenturyPredictiveReal-TimeDashboardsIntegrated Institutional InsightsContinuous Improvements

Evolution of Data for Retention

Institutional ResearchInstitutional EffectivenessBusiness IntelligenceCivitas Learning, Inc.PAR Framework, Inc.

Retention Resources at UMUC

Pre-enrollmentDemographicsEnrollmentLMS EngagementStudent PerformanceTransferMilitary

Factors Included in Predictive Model for Retention at UMUC

CampusClass LoadMilitary StatusAcademic PerformancePayment Method

Key Factors for Retention at UMUC

One year retention (year over year measured with a cohort) Re-enrollment (term to term metric that includes all students) Successful course completion (percentage of students receiving a successful grade) Graduation (1,2,3,4,5, and 10 year rate tracks the graduation status of the starting

cohort over time)

Metrics at UMUC

Curriculum Redesign (2010)8-week Standard Sessions (2010)Community College Transfer (2010)Registration Policy (2013)Onboarding (2014)Just-in-Time Messages (2014)

Retention Initiatives

Discussion

How will you begin, or improve, your analytics journey at YOUR institution?

Elements of a Data Model

Use modeling to Test likely impact on retention when new initiatives or planned interventions are undertaken

Create models that build out retention impact by segments, e.g., demographics, academic programs, persistence, etc.

Continual Improvement

Design Intervention

Collect Data

Analyze Data

Refine or Sunset

DISCUSSION

THANK YOU FOR YOUR INTEREST