1. Predicting Academic Performance of University Students
BIT 5534: Applied Business Analytics & Business Intelligence
Team 4: Michael Cerney, Chris Kopinski, and Chris Stewart

2. Agenda 1. Business Problem 2. Data Understanding 3. Data Preparation
4. Modeling (Ordinary Least Squares + Decision Tree)
5. Results
6. Conclusion

3. Business Problem Topic: University student attrition and retention
Business Problem: Identify characteristics/attributes of success or failure for University students
Measure of Success: GPA Performance
What’s to be gained?
Universities can have more successful student recruitment and selection
Universities create programs that encourage success of current students
Miles from home?
Part-time work hours?
Accommodations?
Business Problem
Data Understanding
Data Preparation
Modeling
Results
Conclusion

4. Attribute Description
Data Understanding
Sample of Variable Dictionary
Dataset acquired from JMP textbook website
Attributes are student-centric:
Ex. GPA, College, Accommodations, etc.
GPA identified as dependent variable
Threshold for academic success based on GPA but not intentionally defined
Attribute Name
Variable Type
Attribute Description
GPA
Continuous
GPA while attending university
Miles from Home
Distance from campus
Accomodations_Dorm
Student lives in dorm
Attends Office Hours_Never
Student never attends office hours
College_Business
Student majors in Business
Class_Freshmen
Student is a freshmen
Business Problem
Data Understanding
Data Preparation
Modeling
Results
Conclusion

5. Data Preparation Data Consolidation Data Cleaning Data Transformation
Data Selection:
Academic variables of interest identified
Data Cleaning
Missing Values Report:
No reported values missing
Outlier Detection Report:
No reported outliers
Data Transformation
Dummy variable creation:
College
Attends Office Hours
Accommodations
Class
Data Reduction
Removed variable Return
Removed 23 records that included the Return attribute
Business Problem
Data Understanding
Data Preparation
Modeling
Results
Conclusion

6. Modeling – Ordinary Least Squares
OLS Model
Independent Variable Estimates
Statistically significant and positively correlated with GPA based on (p < 0.05):
College_Business, College_Sciences, College_Engineering
Statistically significant and negatively correlated with GPA (p < 0.05):
Accomodations_Off-campus
Business Problem
Data Understanding
Data Preparation
Modeling
Results
Conclusion

7. Modeling – Decision Tree
Decision Tree contains 7 splits and records:
First split = yes/no for Business School
Business school in general has higher GPA (2.56 vs 2.44)
Second split = Business School + yes/no on off campus accomodations
Off campus has a higher GPA (2.62 vs 2.51) 1 2
Business Problem
Data Understanding
Data Preparation
Modeling
Results
Conclusion

8. Results – K Fold Cross-Validation
K-Fold Cross-Validation Technique
Dataset was partitioned into five subsets (215 records each)
Each training set contain 80% of the dataset, formed into a unique combination of subsets
Each validation set contain 20% of the dataset (1 unique subset)
Training
Sample
Subsets
Validation
Subset 1
A,B,C,D E 2
B,C,D,E A 3
C,D,E,A B 4
D,E,A,B C 5
E,A,B,C D
Business Problem
Data Understanding
Data Preparation
Modeling
Results
Conclusion

9. Results OLS Model Results Decision Tree Model Results
Confirmed the statistically significant variables (Business, Engineering, Science)
Living off campus showed statistical significance for higher GPA scores
An average (P<0.1) for 5 out of 5 training sets identified that Attends Office Hours Never, displayed a high correlation to lower GPA scores
Decision Tree Model Results
Students that were enrolled into the college of business and science, and that lived off campus, maintain a higher GPA compared to the students that lived on campus
Students that were enrolled into the college of engineering, and lived on campus, performed better than the students that lived off campus
Business Problem
Data Understanding
Data Preparation
Modeling
Results
Conclusion

10. Conclusion Encourage on-campus students to attend office hours
Obtain new perspectives by interviewing students and staff to further investigate low GPA scores
Promote mentorship program
Increase GPA scores and lower attrition rates
Business Problem
Data Understanding
Data Preparation
Modeling
Results
Conclusion