Era of Big Data and Predictive Analytics in Higher Education

8/11/2019 Era of Big Data and Predictive Analytics in Higher Education

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Welcome to the Era of Big Data and Predictive Analytics in Higher Education

Ellen WagnerWICHE Cooperative for Educational Technologies

Joel HartmanUniversity of Central Florida



The Focus of this Session

This session will present an introduction to the emerging andevolving topics o f “Big Data” and predictive analytics particularlyas they apply to higher education and the use of data to improvestudent persistence and outcomes.

An overview of Big Data, an introduction to the PredictiveAnalytics Reporting (PAR) Framework, and an institution’sperspective on these issues along with their implementation ofanalytics will be presented.



Postsecondary Education andthe “New Normal”

• Unprecedented demands for Accountability, Efficiency,Effectiveness

• Increased expectations for greater transparency

• A recognition that shared services are more than just a goodidea 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 EducationLegislative 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 anincreased focus on productivity and flexibility for institutionsand students



Costs increase and completion rates

Source: New York Times; NCES

0

10

20

30

40

50

60

70

1 9 9 6

1 9 9 7

1 9 9 8

1 9 9 9

2 0 0 0

2 0 0 1

2 0 0 2

2 0 0 3

2 0 0 4

2 0 0 5

2-yr colleges

4-yr colleges

Graduation rates at 150% of time

Cohort year



The need to “flip the curve”

Higherenrollments

Morecompletions

Deeperlearning

outcomes

Rising

expectations

Constrainedresources

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

Limitedseat

capacity

Budgetcuts

Decliningfamily

ability topay

90% of communitycolleges in 2010 and

69% in 2011

Additional 300k to 1million credentials

needed per year

Demands of globalized,information economy

32% of communitycollege students unableto enroll in classes; CA

turning away up to 670kstudents per year

58% of communitycollege budgets cut in

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

competition withhealthcare

Student load debt nowgreater than all

consumer loan debt



Innovation and EducationalTransformation

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

• Innovation generally refers to the creation of better or moreeffective products, processes, technologies, or ideas thataffect markets, governments, and society.

• Technologies frequently featured in today’s mix of solutionsfor 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 powernecessary for grinding through calculations and turning themass of numbers into meaningful patterns.

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

• Predictive techniques, such as neural networks and decisiontrees , 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 ModelsProvide Guidance

Courtesy Phil Ice



Big Data and Analytics andFrameworks, Oh, My



BIG DATA AND ANALYTICS ARETAKING HIGHER EDUCATION BYSTORM



Where to Begin?????

• Uncertainty about where to start

– No established industry best practice about what tomeasure

–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







Making Data Matter

Gatherthe data

Turn thedata into

information

Use theinformation tohelp learners









Institutional Partners

American Public UniversitySystem*Ashford UniversityBroward CollegeCapella UniversityColorado Community CollegeSystem*Lone Star College SystemPenn State World CampusRio Salado College*Sinclair Community College

Troy UniversityUniversity of Central FloridaUniversity of Hawaii System*

University of IllinoisSpringfield*University of MarylandUniversity College

University of Phoenix* Western Governors University

http://www.phoenix.edu/

http://www.phoenix.edu/



Predicated on a frameworkof common data definitions

• Common data definitions at the foundation ofreusable predictive models and meaningfulcomparisons.

• Common data definitions openly published viaa cc licensehttps://public.datacookbook.com/public/institutions/par

https://public.datacookbook.com/public/institutions/par










Multi-institutional data

InstitutionalData

College Data

ProgramData

Classroom/Instructor

Data

Student Data

LMSDATA

33 Variables

and“common”definitionsfrom POC

>70 variablesand growingduringimplementation







What are we going to DOwith what we’ve learned????



Some of PAR’s “Products”

V a l i d a t e d M u

l t i -

I n s t i t u t i o n a l

D a t a s e t

Reflective Report

BenchmarkReports

Aggregate Models Policy

InstitutionalModels Local Intervention

Student WatchList

ComparativeInterventions



Actionable Predictive Models



PAR Student Success Matrix

Powerful Framework for benchmarking studentservices and interventions

Q ifi d i i ff i

Q ifi i f I i Eff i



Quantified intervention effectivenessresultsQuantified intervention effectiveness results

Quantification of Intervention Effectiveness



Reusablepredictivemodels

Common Definitionsof Risk

Student levelwatch lists for

targetedinterventions

Multi-Institutional collaboration

Measurableresults

PREDICT

ACT

RESULTS

Common Definitionsof interventions

Scalable cross-institutional improvements





THANKS for your interest

http://parframework.org

http://wcet.wiche.edu

http://parframework.org/

http://wcet.wiche.edu/

http://wcet.wiche.edu/

http://parframework.org/



Big Data

&UCF Student

Success1





An Information Architecture

Policy, security, technology infrastructure,software, and peopleHierarchy of users and information needsHierarchy of tools and methodsFull-service to self-service supportIn support of information-driven planningand decision making



Analytics / Data Science

The extraction of hidden predictiveinformation 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 familiarityInability to associate important businessproblems with big data solutionsUsers or executives rooted in a“retrospective” or “green bar” mentality CostNo data warehouse or analytical toolsData quality issues“Uncollected data cannot be analyzed”





Student Success Initiative Goals

Increase student completion ratesReduce time to degreeMinimize excess credit hour accumulation

7



8

BIGDATA

DEGREEPROGRAMSUPPORT

INTERVENTIONS

PROGRESS

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

PeopleSoftDegree Audit

Mapping &Tracking

Core ServicesIntervention

SupportPrograms

Intervention

AcademicSupport

ProgramsIntervention



Different Levels of Insight

1. How many logins, page views,and other metrics haveoccurred over time?

2. What were the coursecompletion rates for aparticular program over time?What were the attributes ofthe students who didn’tsuccessfully complete?

3. Which tools are being used incourses the most?

1. Which students are exhibitingbehaviors early in the semesterwhich put them at risk for

dropping or failing a course?2. What is the predicted course

completion rate for a particularprogram? Which students arecurrently at risk for completingand why?

3. Which tools and content in thecourse are directly correlatedto student success?

Descriptive Analytics Predictive Analytics

9

Civitas Learning and PAR



Civitas Learning and PARProject: Insights from Big Data

Translate complex data into real-time, personalizedrecommendations to inform decisions and

interventions thatlead to student success

10











15



Big Data

&UCF Student

Success

Documents

Era of Big Data and Predictive Analytics in Higher Education