Welcome to the Era of Big Data and Predictive

Analytics in Higher Education

Ellen Wagner

WICHE Cooperative for Educational Technologies

Joel Hartman

University of Central Florida

The Focus of this Session

This session will present an introduction to the emerging and evolving topics of “Big Data” and predictive analytics particularly as they apply to higher education and the use of data to improve student persistence and outcomes.

An overview of Big Data, an introduction to the Predictive Analytics Reporting (PAR) Framework, and an institution’s perspective on these issues along with their implementation of analytics will be presented.

Postsecondary Education and

the “New Normal”

• Unprecedented demands for Accountability, Efficiency, Effectiveness

• Increased expectations for greater transparency

• A recognition that shared services are more than just a good idea that somebody else should do

• More competition than ever before.

We Can Run…But We Can’t Hide

• New Approaches to the New Normal: 2012 Higher Education Legislative Recap in the West (Nov 27, 2012) (http://www.wiche.edu/info/publications/PI-2012PolicyInsights )

• Notable issues: postsecondary finance, including attempts to implement a new wave of outcomes-based funding; completion, accountability and major governance changes.

• Specific issues include adult learners, workforce development, and the implementation of Common Core Standards.

• Tight budgets will continue to impact higher ed leading to an increased focus on productivity and flexibility for institutions and students

Costs increase and completion rates

Source: New York Times; NCES

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10

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30

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50

60

70

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96

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2-yr colleges

4-yr colleges

Graduation rates at 150% of time

Cohort year

The need to “flip the curve”

Higher

enrollments

More

completions

Deeper

learning

outcomes

Rising expectations

Constrained resources

Source: 2011 Community Colleges and the Economy, AACC/Campus Computing Project, April 2011; Community College Student Survey, Pearson Foundation/Harris Interactive, Field dates: September 27th through November 4th, 2010

Limited

seat

capacity

Budget

cuts

Declining

family

ability to

pay

90% of community colleges in 2010 and

69% in 2011

Additional 300k to 1 million credentials needed per year

Demands of globalized, information economy

32% of community college students unable to enroll in classes; CA

turning away up to 670k students per year

58% of community college budgets cut in

2011-2012; 41% of cuts >5%; long-term

competition with healthcare

Student load debt now greater than all

consumer loan debt

Innovation and Educational

Transformation

• The term innovation derives from the Latin word innovare "to renew or change."

• Innovation generally refers to the creation of better or more effective products, processes, technologies, or ideas that affect markets, governments, and society.

• Technologies frequently featured in today’s mix of solutions for solving problem and promoting innovation

Tech Trend and Analytics

• Data Warehouses and “the Cloud” make it possible to collect, manage and maintain massive numbers of records.

• Sophisticated technology platforms provide computing power necessary for grinding through calculations and turning the mass of numbers into meaningful patterns.

• Data mining uses descriptive and inferential statistics —moving averages, correlations, and regressions, graph analysis, market basket analysis, and tokenization – to look inside those patterns for actionable information.

• Predictive techniques, such as neural networks and decision trees, help anticipate behavior and events.

Why the Emergence of “Big Data”?

• Expectations for accountability to stakeholders

• Demands for evidence to guide and support decision-making

• Finding metrics that matter to institutions AND individuals

• Technology platforms provide a means to the end.

Where are we headed? Business Models Provide Guidance

Courtesy Phil Ice

Big Data and Analytics and

Frameworks, Oh, My

BIG DATA AND ANALYTICS ARE

TAKING HIGHER EDUCATION BY

STORM

Where to Begin?????

• Uncertainty about where to start

– No established industry best practice about what to measure

– No established industry best practice around methodology

• Institutional Culture, Learning Culture and Status Quo

– Enterprise concern about what the data will show

– Competing priorities and lack of incentive for collaboration between different groups

• Siloed data across the enterprise doesn’t help.

13

Sage Road Solutions LLC

Evidence-based decision-making

Success and decision making are predicated on access to data

Understanding strengths and weaknesses is dependent on having access to all data within the enterprise

Data tells us what has happened and improves strategic planning moving forward

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What is the PAR Framework?

• A “big data” analysis effort identify drivers related to loss and momentum and to inform student loss prevention

• WCET member institutions voluntarily contribute de-identified student records to create a single federated database.

Making Data Matter

Gather the data

Turn the data into

information

Use the information to help learners

Institutional Partners

American Public University System* Ashford University Broward College Capella University Colorado Community College System* Lone Star College System Penn State World Campus Rio Salado College* Sinclair Community College

Troy University

University of Central Florida

University of Hawaii System*

University of Illinois Springfield*

University of Maryland University College

University of Phoenix*

Western Governors University

Predicated on a framework

of common data definitions

• Common data definitions at the foundation of reusable predictive models and meaningful comparisons.

• Common data definitions openly published via a cc license https://public.datacookbook.com/public/institutions/par

Multi-institutional data

Institutional Data

College Data

Program Data

Classroom/Instructor

Data

Student Data

LMS DATA

33 Variables and “common” definitions from POC

>70 variables and growing during implementation

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Making Data Matter Via Modeling

• Model building is an iterative process

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

What are we going to DO with what we’ve learned????

Some of PAR’s “Products”

Val

idat

ed M

ult

i-In

stit

uti

on

al

Dat

aset

Reflective Report

Benchmark Reports

Aggregate Models Policy

Institutional Models

Local Intervention

Student Watch List

Comparative Interventions

Actionable Predictive Models

PAR Student Success Matrix

Powerful Framework for benchmarking student services and interventions

Quantified intervention effectiveness

results

Quantified intervention effectiveness results

Quantification of Intervention Effectiveness

Reusable predictive models

Common Definitions of Risk

Student level watch lists for

targeted interventions

Multi-Institutional collaboration

Measurable results

PREDICT

ACT

RESULTS

Common Definitions of interventions

Scalable cross-institutional improvements

Partner Perspectives:

The University of Central Florida

Dr. Joel Hartman

Vice Provost for Information Technologies and Resources and CIO

THANKS for your interest

http://parframework.org

http://wcet.wiche.edu

Big Data

&

UCF Student

Success

1

From Data To Information

Era Evolutionary Step Technologies Perspective

1960s-

1970s

Data Collection Computers, tapes, disks Retrospective, static

data delivery

1980s Data Access RDBMS, SQL, ODBC Retrospective, dynamic

data delivery

1990s Data Warehouses, Data

Marts, Decision Support

Tools, BI

Data warehouses,

data marts, OLAP

Retrospective, dynamic

data delivery

2000s Data Mining /

“Big Data”

Models, algorithms,

fast computers,

massive databases,

dashboards

Prospective, proactive

information delivery,

visualization, and

exploration

Source: Kurt Thearling

An Information Architecture

Policy, security, technology infrastructure,

software, and people

Hierarchy of users and information needs

Hierarchy of tools and methods

Full-service to self-service support

In support of information-driven planning

and decision making

Analytics / Data Science

The extraction of hidden predictive

information from large databases

◦ determination of “rules” working in the target

environment, but hidden in the data

◦ future events, trends, behaviors

◦ can “tag” individuals

◦ predictive capabilities

Barriers

Lack of executive vision or familiarity

Inability to associate important business

problems with big data solutions

Users or executives rooted in a

“retrospective” or “green bar” mentality

Cost

No data warehouse or analytical tools

Data quality issues

“Uncollected data cannot be analyzed”

UCF Information Architecture

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Student Success Initiative Goals

Increase student completion rates

Reduce time to degree

Minimize excess credit hour accumulation

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8

BIG

DATA DEGREE

PROGRAM

SUPPORT

INTERVENTIONS

PROGRESS

P.R.O.G.R.E.S.S. – Probing to Remove Obstacles toward Graduation and Retention for Enrolled Student Success

PeopleSoft

Degree Audit

Mapping &

Tracking

Core Services

Intervention

Support

Programs

Intervention

Academic

Support

Programs

Intervention

Different Levels of Insight

1. How many logins, page views,

and other metrics have

occurred over time?

2. What were the course

completion rates for a

particular program over time?

What were the attributes of

the students who didn’t

successfully complete?

3. Which tools are being used in

courses the most?

1. Which students are exhibiting

behaviors early in the semester

which put them at risk for

dropping or failing a course?

2. What is the predicted course

completion rate for a particular

program? Which students are

currently at risk for completing

and why?

3. Which tools and content in the

course are directly correlated

to student success?

Descriptive Analytics Predictive Analytics

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Civitas Learning and PAR

Project: Insights from Big Data

Translate complex data into real-time, personalized

recommendations to inform decisions and

interventions that

lead to student success 10

15

Big Data

&

UCF Student

Success

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