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Predictive Analytics Proof of Concept (POC)

September 2014

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 Additional Information on the Sac State Predictive Analytics POC

ECUCAUSE 2014 Poster Session

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Proof of Concept (POC) Objectives

1. Provide predictive insights for a university-wide strategic

issue/program (e.g. student success and student retention)

2. Demonstrate the capability of predictive analytics for broaderapplication

3. Develop expertise with IBM SPSS Modeler in partnership with the

vendor and key campus leaders

4. Identify gaps in the data and next steps for architecting anddeploying a predictive analytics solution

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Indicators:

1. Enroll full time

2. Earn summer credits

3. Complete a college success

course or first-yearexperience program

Subset of Indicators and Milestones identified by the Institute for Higher Education Leadership

and Policy (IHELP): “Student Flow Analysis: CSU Student Progress Toward Graduation”  

Milestones:

1. First semester grade point

average

2. Second semester grade point

average

*    

Predictive Analytics Journey

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SPSS Modeler “Stream” 

Using Factors from Published Study

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Inside the “Super Node” 

 Additional Data Prep

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“ Auto Prep” Option in SPSS Modeler  Choose Speed, Accuracy, or Manual

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How Good was the POC Model?

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SPSS ModelerPredictions for Each Student in Cohort

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Predictive Analytics POCLessons Learned

1. Learned how to use IBM SPSS Modeler

2. Data Prep is key – and time consuming!

3. Consider moving some of the Data Prep to the ETL layer (i.e. model the data so it can easily be

used at input for analytics)

4. You must “know your data” 

5. You must be familiar with statistical methods to prep the data properly and to understand the

results

6. Optimal Predictive Analytics Project Team: Data Modeler, BI Analyst, Subject Matter Expert from

functional area, and Data Scientist

7. Correlation vs. Cause

8. The output may be one step in developing advising programs, identifying advising cohorts or for

advising individuals; however, caution should be taken in directly advising a student based on onepredictive model looking 5 years out

9. Predictive analytics is an on-going, iterative process

10. There is an opportunity to write the predicted outcomes to the data warehouse and use them to

track the usefulness of the model and to create dashboards to track the success of resulting

programs

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 Additional POC Work

1. In addition to focusing on the IHELP indicators and milestones,

several models using a broader set of data from the data

warehouse were developed

2. Experimented with different cohort years and different targets

3. Used IBM SPSS Modeler to develop a basic POC Faculty

Retention Model

4. Used IBM SPSS Modeler for descriptive analytics for AD ASTRA

event and scheduling data

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Predictive Analytics

Next Steps

1. Link to campus strategic plan, identify an opportunity for predictive analytics to

contribute to its success, and build target models with the “optimal team” as described

previously (tight collaboration with campus functional areas)

2. Continue to develop models focused on student success, but explore other areas such

as university advancement, scheduling, etc.

3. Move from using flat file extracts to connecting IBM SPSS Modeler directly to the data

warehouse

4. Develop data models and ETLs to better prep the data for predictive analytics and data

mining

5. Identify missing data or data gaps and close the gaps if possible with the data that we

have

6. Partition data to develop the model on a subset of data and then test its predictive

power on the remaining set

7. Continue to learn and build expertise with SPSS Modeler and its capabilities as well as

continue to build expertise in statistical methods in general