improved governance through the application of advanced research techniques data mining and...

8/9/2019 Improved Governance Through the Application of Advanced Research Techniques Data Mining and Predictive Analytics - Dr. Nick a

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Improved Governance Throughthe Application of Advanced

Research Techniques:Data Mining/Predictive Analytics

Nick B. Fontanilla, Ph.D.


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UNDECIDED VOTERS ACROSS TIME

5

0%

5%

10%

15%

20%

25%

30%

35%

40%

45%

40.5%

34.3%

27.0%

Oct-09 Dec-09 Feb-10

10%?
http://campaigns%20micro-target%20political%20messages%20to%20voters%20-%207%2014%2008%20-%20san%20francisco%20news%20-%20abc7news.com_%28converted%29.avi/http://campaigns%20micro-target%20political%20messages%20to%20voters%20-%207%2014%2008%20-%20san%20francisco%20news%20-%20abc7news.com_%28converted%29.avi/


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Evolution of Research


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Web 2.0


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User Participation


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Role of Social Media in Research


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Researcher of the Future


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Multi-Modal

A survey that is administered in multiple

research modes, for example, web-based and

phone-based, web-based and paper-based,

etc.


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Benefits of Multi-Modal

A low cooperation or response rate does more

damage in rendering a surveys results

questionable than a small sample because

there may be no valid way scientifically ofinferring the characteristics of the population

represented by the non-respondents.

American Association for Public Opinion Research

Best Practices for Survey and Public Opinion Research


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Why Multi-Mode

Increased response rates, reduced non-response error

Multi-phase approaches have provento be =effective

E.g. media studies with large, burdensome booklets.

Improved sample coverage

Hard to reach demographics Cell phone only households

Respondent centric

Offer participants choices in how and when to complete

E.g., customer research, where respondents are highly valued.

Research spanning intercontinental boundaries Mode dependent on local infrastruture and customs

Reducing cost

Balance increased admin vs. reduced interviewing cost


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Traditional


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Web 2.0


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Web 2.0


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Business Challenge for Institutions

Source: Educause Core Data Service, Fiscal Year 2005 Summary Report, Brian L. Hawkins and Julia A. Rudy, November 2006.

The need to provide better campus decision

support systems with an integrated view of datais critically important to campuses in order to

manage the complexities of our institutions in aturbulent market environment.

- Educause


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Predictive Analytics

Predictive analysis helps connect data toeffective action by drawing reliable

conclusions about current conditions and

future events.

Gareth Herschel, Research Director, Gartner Group
http://crime%20watch.mp4/


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What is Data Mining?

Data mining is the process of discovering

meaningful new correlation, patterns and

trends by sifting through large amounts of

data stored in repositories, and by using

pattern recognition technologies, as well as

statistical and mathematical techniques.


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What is Data Mining?

"Data mining offers firms in many industries the

ability to discover hidden patterns in their data

patterns that can help them understand

customer behavior and market trends.


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Types of Data Mining

Supervised Data Mining

Known outcome

Example: Graduation Database

Students who completed their studies vs. those whodropped out

Unsupervised Data Mining

Particular groupings or patterns are unknown Example: Student Course Database

Little is known about which courses are usually taken as agroup


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Data Mining Applications in Higher Education

By Jing Luan, Ph.D.Vice Chancellor, Educational Services and PlanningSan Mateo County Community College DistrictAnd Founder, Knowledge Discovery Laboratory

Formerly

Chief Planning and Research Officer, Cabrillo College


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Data Mining Applications in Academe

Who are likely to pursuetheir application in ouruniversity?

Which subjects arelikely taken togetherby our students?

Which subject typesare associated withcertain student types?

What typology is ourstudents classifiedinto?

Who are likely to shiftdegree courses?


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Data Mining Applications in Academe

Who are likely not tograduate on time?

Who are likely to transferto another university?

How would weallocate efficientlytime, manpower, andbudget?

Who among our alumniare likely to offerpledges?

How would we maximize the informationfrom comments / opinions provided byour students in their evaluation?


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Case Study 1:Creating meaningful learning outcome typologies

Challenge: What do institutions know about their students?

A typical suburban community college with anenrollment of 15,000 traditionally identifies its

students as: Transfer Oriented ,

Vocational Education Directed, or

Basic Skill Upgraders

Classifications are based on students initialdeclarations of educational goals at enrollment.

To illustrate further the differences between eachstudent type


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Solution: Two-Step and K-Means clustering algorithms Using the general classification, boundaries among clusters

were unclear and dispersed.

Possibly, students initial declaration of goals did not dictate

their academic behavior. Considering educational outcomes and length of study, Two

Step produced the ff clusters, which K-Means validated: Transfers

Vocational Students

Basic Skill Students Students with Mixed Outcomes

Dropouts


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Results:

Improved understanding of students types

Older students tend to take their time.

Younger students with more privileged socioeconomicbackgrounds often took high credit courses and

graduated quickly.

Helped educators and administrators better meetthe needs of varied student groups


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Case Study 2:Academic Planning and Interventions Transfer Prediction

Challenge: More than half of community college students identify

transferring to four-year universities as their goal.

To accurately predict academic outcomes in order to

facilitate timely academic intervention (e.g. studenttransfer)

Solution:

Neural Network and Rule Induction algorithms usingSupervised Data Mining

Predictors: Demogrpahics, Courses Taken, UnitsAccumulated, Financial Aid


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Case Study 2:Academic Planning and Interventions Transfer Prediction

Results:

Enabled the college to accurately identify good

transfer candidates.

Model Accuracy: Neural Net : 72%

Rule Induction (C5.0 and C&RT) : 80%


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Case Study 3:Predicting Alumni Pledges

Challenge:

For a typical urban university of 25,000, the

alumni population can be as ten times as its

enrollment. Universities send mailings to alumni on a regular

basis, even when alumni fail to respond.

Mailing cost > $100K a year. To focus on the alumni most likely to make

pledges


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National Marketing Conference, June 24 and 25, 2010Strategic Marketing Conference for Students, July 20, 2010Agora Youth AwardsAgora ConferenceAgora Awards

Certified Professional Marketers in Asia


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Thank you

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