STUDENT RETENTION PREDICTION USING DATA MINING TOOLS AND BANNER DATA Admir Djulovic Dennis Wilson...
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Transcript of STUDENT RETENTION PREDICTION USING DATA MINING TOOLS AND BANNER DATA Admir Djulovic Dennis Wilson...
NWEUG2015
STUDENT RETENTION PREDICTION
USING DATA MINING TOOLS AND BANNER DATA
Admir Djulovic
Dennis Wilson
Eastern Washington University
Business Intelligence
Coeur d’Alene, Idaho
NWEUG2015
SESSION RULES OF ETIQUETTE
Please turn off you cell phone/pager
If you must leave the session early, please do so as discreetly as possible
Please avoid side conversation during the session
Thank you for your cooperation!
Coeur d’Alene, Idaho
NWEUG2015
INTRODUCTION
Focus: Why first time freshmen students are leaving in the first year?
Benefits of attending this session You will learn how we use Banner and Data Mining tools to
identify students at risk
Learn about factors that influence student retention
We will share our results and findings
Coeur d’Alene, Idaho
NWEUG2015
AGENDA
1. Why first time freshmen students are leaving in the first year?
2. Retention Data Mining Model Creation
3. Results and Findings
4. Future Work
5. Questions
Coeur d’Alene, Idaho
NWEUG2015
WHY STUDENTS ARE LEAVING IN THE FIRST YEAR?
What are the factors that cause student to leave the university?
Pre-enrollment Information (i.e. SAT and ACT test scores)
Poor academic performance
Financial hardship
We want to determine data driven factors that influence student retention
Coeur d’Alene, Idaho
NWEUG2015
RETENTION DATA MINING MODEL CREATION• The model uses existing student and financial data in Banner to give
us a prediction of how many first time freshmen students will or will not return the following Fall term
Coeur d’Alene, Idaho
NWEUG2015
RETENTION DATA MINING MODEL CREATION
• Determine what student attributes would provide the greatest benefit with these constrained• Pre-enrollment information• Financial Information• Housing Information• Financial Aid Information
• Determine what Data Mining Predictive algorithms to use
Coeur d’Alene, Idaho
NWEUG2015
STUDENT ATTRIBUTES USED TO BUILD THE MODEL Special Attributes
ID – unique record identifier RETAINEDNXTYR (Known Outcome/Target variable): Student
retained next year (0: No, 1: Yes)
Pre-Enrollment Attributes Age Gender SAT Scores in Reading, Math and Writing Previous GPA (typically high school GPA)
Term Related Attributes Account Balance Cumulative GPA Successive term GPA Living on or off campus Financial aid received or not
Coeur d’Alene, Idaho
NWEUG2015
STUDENT ATTRIBUTES USED TO BUILD THE MODEL
Coeur d’Alene, Idaho
Table 1: Normalized Weights of Independent Variables Using Relief Statistical Method (All weights above 0.5 are deemed important in determining student retention.)
NWEUG2015
STUDENT ATTRIBUTES USED TO BUILD THE MODEL
Coeur d’Alene, Idaho
Table 2: Normalized Weights of Independent Variables Using Information Gain Statistical Method (All weights above 0.5 are deemed important in determining student retention.)
NWEUG2015
STUDENT ATTRIBUTES USED TO BUILD THE MODEL
Coeur d’Alene, Idaho
Table 3: Normalized Weights of Independent Variables Using Chi Squared Statistics Method (All weights above 0.5 are deemed important in determining student retention.)
NWEUG2015
DATA USED
Coeur d’Alene, Idaho
• First time full time freshmen – Fall cohort (Could be applied to any population)
• Cohort groups of data• Fall 2006 – 2011 Freshmen to train the model• Fall 2013 Freshmen to test model
NWEUG2015
ALGORITHM SELECTION
Coeur d’Alene, Idaho
• The following predictive algorithms have been used in many research paper
Data Mining
Predictive Algorithms
NWEUG2015
TRAINING THE MODEL USING HISTORICAL DATA
Coeur d’Alene, Idaho
• Historical Data:
• From 2006 through 2012
• Test Data:
• 2013 Academic Year
Run Historical
Data through
Algorithms
Compare Accuracy of each Algorithm
to 2013 Data
2006-2012 Data
NWEUG2015
RESULTS AND FINDINGS
Winter Living on Campus vs RETAINEDNXTYR (0:No; 1:Yes)
Coeur d’Alene, Idaho
NWEUG2015
RESULTS AND FINDINGS
Winter Received Financial Aid vs RETAINEDNXTYR (0:No; 1:Yes)
Coeur d’Alene, Idaho
NWEUG2015
HOW COULD THIS RETENTION MODEL HELP?
Provide early warning of students at risk Lists can be provided to different offices for student outreach
Improve student retention
Use it to forecast future student retention
Coeur d’Alene, Idaho
NWEUG2015
FUTURE WORK
Attributes for future consideration Student Attendants List Student Credit Hours Repeat Class Indicator Types of Financial Aid Major College Residency Other Attributes
Coeur d’Alene, Idaho
NWEUG2015
SESSION SUMMARY
We have demonstrated how Banner data and data mining tools are used to identify students at risk
We have demonstrated how predictive models are created and how they work
Factors that contribute to a student’s dropping out
Data mining Algorithms used
Demonstrate how retention models can be used as a early warning system to identify students at risk
Coeur d’Alene, Idaho