Using SAS® Enterprise BI Server 9.2 and Dimensional Modeling Techniques

to Identify Students that May Need Support

QUEST Q1-2014

Data Scientists?

Agenda �  Introduction to UNSW and the Australian School of

Business � Description of the business problem �  4 step methodology

University of New South Wales � UNSW

� Formed in 1949

� More that 50,000 students

� Member of the Group of Eight (Go8)

� Ranked 52 in the QS World University Rankings https://www.unsw.edu.au/sites/default/files/documents/UNSW4009_Miniguide_2012_AW2_V2.pdf

University of New South Wales � Australian School of Business

� Over 12,000 students

� Currently ranked 12th in the world for Accounting and Finance degrees

� Top ranking MBA in Australia

� MBA ranked 48th in the world

http://en.wikipedia.org/wiki/University_of_NSW

Julia Enterprise Data Warehouse � Developed by the Institutional Analysis and Reporting

Office at UNSW with support from UNSW IT �  SAS was installed at UNSW in 2004 as a proof of

concept �  2009 Migrated from SAS 9.1 to 9.2 �  2010 Julia in its current form commenced in SAS

Enterprise BI Server using Kimball dimensional modeling techniques

�  Flagged for replacement by an EDW being developed by UNSW IT using SAS Enterprise BI Server 9.4

Business Problem �  Identification of students potentially at risk

�  Widespread, automated and earlier student advisement related to engagement and performance

�  Student engagement in courses via Learning Management System (LMS) access and activity

�  How do you identify two or three hundred students out of 12000 needing support?

�  Students are often shy in asking for help

Methodology Step 2 –

Analyse for Churn or

Risk Patterns

Step 3 – Build a Repeatable

Model

Step 4 – Apply the

Model

Step 1 – Obtain Good

Customer Data

SAS Enterprise Guide®

Star Schemas in SAS BI Suite

SAS BI Suite

Step One: Obtain Good Customer Data / Build a Good Data Warehouse

Step Two: Analyse for Churn or Risk Patterns Convention in the sector

� Low Social Economic Standing

� Low ATAR (Australian Tertiary Admissions Rank)

� Students with a lower WAM (Weighted Average Mark) Are much more likely to drop out

Step Two: Analyse for Churn or Risk Patterns � What is Risk?

� Low WAM � Churn (Dropping out of UNSW)

�  A number of variables were investigated for Churn and WAM using SAS Enterprise Guide

Step Two: Analyse for Churn or Risk Patterns �  Variables investigated for Churn and WAM using SAS

Enterprise Guide �  Admittance Type - Cross Institutional, Exchange Student, Foundation Studies UNSW, First Year Student, Internal Program

Transfers, Readmit to Program etc.

�  Application Method - Direct or University Admissions Centre

�  Social Economic Standing by Postcode – Based on ABS data

�  Gender – Retention and WAM comparisons

�  Language Spoken at Home

�  High School Math - Subject and Grades

�  Parental Education Level �  English Language Proficiency for international students

�  Residency group – Local or international

�  Students in a program that was not their first choice �  Blackboard and Moodle Usage – Learning Management System �  Moodle grades �  Age as the start of program �  Subjects Failed (tested against churn only)

�  WAM falling (tested against churn only)

Step Two: Analyse for Churn or Risk Patterns

Highest Parental Education Level vs. Retention

Step Two: Analyse for Churn or Risk Patterns

Geo mapping of WAM

Step Three: Build a Repeatable Model �  Decided on three groups of attributes:

�  Current Learning Activities – Given the most weight �  LMS Exam Result Rate �  LMS Access Rate

�  University Study History – Given the second most weight �  Failed This Course Before �  Course Fails �  WAM Drop Level �  WAM Level

�  University Entry Ranks – Given the least weight �  ATAR Score �  High School Math Proficiency �  Ranked Entry Score �  Written English Proficiency �  Total English Proficiency

Step Three: Build a Repeatable Model � We built a linear model fairly

simple, able to be explained (one of the goals)

� Ultimate would be to have multiple models and evolve them over time and potentially select students who show up in the models

� We still don’t KNOW what is happening in the student’s life

Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die by Eric Siege

Step Four: Apply the Model �  Pilot – picked four subjects and ran a pilot program

doing intervention �  Showed that the model was helping us find students we need to

talk to � Allowed focus on building methods for intervening

� Output of model fed into CRM from Semester 2 2013

�  2014 – Beginning to focus on risk for specific courses such as Math intensive course, possible expansion to include Physics

Recap Step 2 –

Analyse for Churn or

Risk Patterns

Step 3 – Build a Repeatable

Model

Step 4 – Apply the

Model

Step 1 – Obtain Good

Customer Data

SAS Enterprise Guide®

SAS Enterprise BI Server SAS® Data Integration Studio SAS® Web Report Studio

SAS Enterprise BI Server SAS Data Integration Studio SAS Web Report Studio

Conclusion � Questions?

� Contact David Waters davidmwaters@yahoo.com Ph: 0408-074082 Linkedin: https://www.linkedin.com/in/davidmwaters