1. EDUCAUSE Annual Conference
Who are your at-risk Students? Using Data Mining to Target Intervention Efforts
Lalitha Agnihotri , Ph.D., Senior Systems Analyst, DWH
Alex Ott , Ed.D., Associate Dean, Academic & Enrollment Services
Niyazi Bodur, Ph.D., VP, Information Technology & Infrastructure
New York Institute of Technology
EDUCAUSE Annual Conference
October 16th, 2013

2. Presentation Description and Goals
Learn how to improve targeted intervention by building a model to identify and classify at-risk students using data at your institution.
Gain an understanding of the complete life cycle of the At-Risk Student Identification Model.

3. Targeted Intervention for At Risk Students
The Goal: Early targeted intervention based on risk factors for each at-risk student to improve retention
Rationale for Key Elements:
Early
Targeted intervention
Risk factors for each student

4. Before the Model, All We Had Was…

5. Students At Risk (STAR) Model Version 1.0
Data sources:
Admissions data
Registration/Placement test data
Survey data
Method:
Combine all risk variables into an aggregated measure.
Alex to insert an excel

6. Version 1.0 Report Output:

7. Major Challenges with STAR 1.0
Limited attributes.
Attributes of unknown strength, relevance, or even direction.
Attributes equally weighted.
Static Excel document: Big effort in getting all the attributes in one place.
Major limitations ( limited factors, equally treated, applied simple logic calculation with manual process, no data mining applied).

8. Data Mining

9. Data Mining Classification
Given a collection of records (training set )
Each record contains a set of attributes, one of the attributes is the class.
Goal: previously unseen records should be assigned a class as accurately as possible.
Find a model for class attribute as a function of the values of other attributes.
Select the model that performs the best.
Student ID
Attributes
Class

10. STAR Model: Version 2.0 with Data Mining and Automated Tools
Built and automated the full dataset in our Data Warehouse
Used Data Mining tools (SQL Server Analysis Services) to train multiple dynamic statistical models
Enterprise solution
SSAS Modeling
DMX Prediction Query
SSRS Report
SQL Build Data

11. Models Trained Logistic Regression Logistic Regression Naïve Bayes
Ensemble
Logistic Regression
Naïve Bayes
Neural Network
Decision Trees
Logistic Regression
Naïve Bayes
Neural Network
Decision Trees

12. Data Mining Knowledge Discovery: BIG Picture
Lalitha to update the graphs with new labels

13. Data Mining Knowledge Discovery: Detailed Picture

14. Model Significance And Results
You can re-org

15. So How Did the Model Actually Perform?
Change this slide

16. Key Takeaways
Success depends on productive partnership between IT and business.
Data is the KEY.
Data mining is a process.
Select attributes based on (retention) research and particulars of your school.
a.       Data mining fundamentally is process (setup a high level goal –blue print, have departmental goal – detail drawing, physical, and data mining tools implementation, make a reality)  
b.      Successful implementation needs a good partnership business department and IT
c.       Data is key. No data no talking.

17. Questions?
Lalitha Agnihotri,
Alexander Ott,
Niyazi Bodur,