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NWEUG 2015 STUDENT RETENTION PREDICTION USING DATA MINING TOOLS AND BANNER DATA Admir Djulovic Dennis Wilson Eastern Washington University Business Intelligence Coeur d’Alene, Idaho 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 NWEUG 2015 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 NWEUG 2015 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 NWEUG 2015 WHY STUDENTS ARE LEAVING IN THE FIRST YEAR? Coeur d’Alene, Idaho NWEUG 2015 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 NWEUG 2015 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 NWEUG 2015 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 NWEUG 2015 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 NWEUG 2015 STUDENT ATTRIBUTES USED TO BUILD THE MODEL Table 1: Normalized Weights of Independent Variables Using Relief Statistical Method (All weights above 0.5 are deemed important in determining student retention.) Coeur d’Alene, Idaho NWEUG 2015 STUDENT ATTRIBUTES USED TO BUILD THE MODEL Table 2: Normalized Weights of Independent Variables Using Information Gain Statistical Method (All weights above 0.5 are deemed important in determining student retention.) Coeur d’Alene, Idaho NWEUG 2015 STUDENT ATTRIBUTES USED TO BUILD THE MODEL Table 3: Normalized Weights of Independent Variables Using Chi Squared Statistics Method (All weights above 0.5 are deemed important in determining student retention.) Coeur d’Alene, Idaho NWEUG 2015 DATA USED • 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 Coeur d’Alene, Idaho NWEUG 2015 ALGORITHM SELECTION • The following predictive algorithms have been used in many research paper Coeur d’Alene, Idaho NWEUG 2015 TRAINING THE MODEL USING HISTORICAL DATA • Historical Data: • • From 2006 through 2012 Test Data: • 2013 Academic Year Coeur d’Alene, Idaho NWEUG 2015 MODEL(S) TRAINING AND TESTING PHASE Coeur d’Alene, Idaho NWEUG 2015 MODEL(S) ACCURACY Coeur d’Alene, Idaho NWEUG 2015 MODEL(S) ACCURACY CONT. Coeur d’Alene, Idaho NWEUG 2015 APPLYING THE MODEL(S) USING THE NEW DATASET Coeur d’Alene, Idaho NWEUG 2015 APPLYING MODELS USING NEW DATASET Academic Year 2013-2014 Coeur d’Alene, Idaho NWEUG 2015 RESULTS AND FINDINGS Coeur d’Alene, Idaho NWEUG 2015 RESULTS AND FINDINGS Winter Balance vs RETAINEDNXTYR (0:No; 1:Yes) Coeur d’Alene, Idaho NWEUG 2015 RESULTS AND FINDINGS Winter Living on Campus vs RETAINEDNXTYR (0:No; 1:Yes) Coeur d’Alene, Idaho NWEUG 2015 RESULTS AND FINDINGS Winter Received Financial Aid vs RETAINEDNXTYR (0:No; 1:Yes) Coeur d’Alene, Idaho NWEUG 2015 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 NWEUG 2015 EXAMPLES Not returning due to the low GPA (0:No; 1:Yes) Coeur d’Alene, Idaho NWEUG 2015 EXAMPLES CONT. Not returning due to the high balance (0:No; 1:Yes) Coeur d’Alene, Idaho NWEUG 2015 FUTURE WORK Coeur d’Alene, Idaho NWEUG 2015 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 NWEUG 2015 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 NWEUG 2015 QUESTIONS & ANSWERS Coeur d’Alene, Idaho NWEUG 2015 THANK YOU! Admir Djulovic, Dennis Wilson Coeur d’Alene, Idaho NWEUG 2015
