1. STUDENT RETENTION PREDICTION USING DATA MINING TOOLS AND BANNER DATA Admir Djulovic Dennis Wilson Eastern Washington University Business Intelligence Coeur d’Alene, Idaho

2. 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

3. 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

4. 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

5. WHY STUDENTS ARE LEAVING IN THE FIRST YEAR? Coeur d’Alene, Idaho

6. 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

7. 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

8. 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

9. 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

10. 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.)

11. 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.)

12. 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.)

13. 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

14. ALGORITHM SELECTION Coeur d’Alene, Idaho The following predictive algorithms have been used in many research paper

15. TRAINING THE MODEL USING HISTORICAL DATA Coeur d’Alene, Idaho Historical Data: From 2006 through 2012 Test Data: 2013 Academic Year

16. MODEL(S) TRAINING AND TESTING PHASE Coeur d’Alene, Idaho

17. MODEL(S) ACCURACY Coeur d’Alene, Idaho

18. MODEL(S) ACCURACY CONT. Coeur d’Alene, Idaho

19. APPLYING THE MODEL(S) USING THE NEW DATASET Coeur d’Alene, Idaho

20. APPLYING MODELS USING NEW DATASET Coeur d’Alene, Idaho  Academic Year 2013-2014

21. RESULTS AND FINDINGS Coeur d’Alene, Idaho

22. RESULTS AND FINDINGS  Winter Balance vs RETAINEDNXTYR (0:No; 1:Yes) Coeur d’Alene, Idaho

23. RESULTS AND FINDINGS  Winter Living on Campus vs RETAINEDNXTYR (0:No; 1:Yes) Coeur d’Alene, Idaho

24. RESULTS AND FINDINGS  Winter Received Financial Aid vs RETAINEDNXTYR (0:No; 1:Yes) Coeur d’Alene, Idaho

25. 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

26. EXAMPLES  Not returning due to the low GPA  (0:No; 1:Yes) Coeur d’Alene, Idaho

27. EXAMPLES CONT.  Not returning due to the high balance  (0:No; 1:Yes) Coeur d’Alene, Idaho

28. FUTURE WORK Coeur d’Alene, Idaho

29. 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

30. 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

31. QUESTIONS & ANSWERS Coeur d’Alene, Idaho

32. THANK YOU! Admir Djulovic, Dennis Wilson Coeur d’Alene, Idaho