educa leveraging analytics final

Leveraging Analytics to Improve Student Success

Karen Vignare, University Maryland University College

@kvignare

Ellen 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 Education

Learning Analytics

Best way to teach and learn

Learner Analytics

Best way to support students

Organizational Analytics

Best 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

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. 9

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 does

PAR 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

learner characteristics

learner behaviors

fit/feelings of belonging

other learner support

course/program characteristics

instructor behaviors

time connection entry progress completion

predictors

©PAR Framework 2015

SMALL GROUP DISCUSSION

How 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 1949

Part of the University System of Maryland

About UMUC

20th Century

Historical

Longitudinal

Warehouse

Siloed

External

Reporting

21st Century

Predictive

Real-Time

Dashboards

Integrated Institutional Insights

Continuous Improvements

Evolution of Data for Retention

Institutional Research

Institutional Effectiveness

Business Intelligence

Civitas Learning, Inc.

PAR Framework, Inc.

Retention Resources at UMUC

Pre-enrollment

Demographics

Enrollment

LMS Engagement

Student Performance

Transfer

Military

Factors Included in Predictive Model for Retention at UMUC

Campus

Class Load

Military Status

Academic Performance

Payment 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

71.2 72 71.6 73.2

60.5 59.5 61.566

0

10

20

30

40

50

60

70

80

Fall 2011 Fall 2012 Fall 2013 Fall 2014

Stateside

Overseas

Retention Rates and Headcounts

47,416 46,213 41,197 41,356

Operationalize

successful tests;

“Lessons

Learned” fed

back

to body of

knowledge

Student Retention Enterprise Framework

Diagnosed

from internal

data and

external

research

Root cause

analysis

performed

and search

of existing

body of

knowledge

solutions

Work within

Governance

Structure

Levers pulled

here;

Measure

success &

ROI;

Quarterly

Reviews

Retention Root Cause Identification & Analysis

Retention

Opportunity

Problem

AnalyzedHypothesis

Generated

Test &

Learn Cycle

Operationaliz

e or Re-

create

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