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 Rankingshttps://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 ServerSAS® Data Integration Studio SAS® Web Report Studio

SAS Enterprise BI ServerSAS 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