STUDENT RETENTION PREDICTION USING DATA MINING TOOLS AND BANNER DATA Admir Djulovic Dennis Wilson...

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STUDENT RETENTION PREDICTION

USING DATA MINING TOOLS AND BANNER DATA

Admir Djulovic

Dennis Wilson

Eastern Washington University

Business Intelligence

Coeur d’Alene, Idaho

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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!


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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


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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


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WHY STUDENTS ARE LEAVING IN THE FIRST YEAR?


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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


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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


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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


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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


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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.)

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Table 2: Normalized Weights of Independent Variables Using Information Gain Statistical Method (All weights above 0.5 are deemed important in determining student retention.)

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Table 3: Normalized Weights of Independent Variables Using Chi Squared Statistics Method (All weights above 0.5 are deemed important in determining student retention.)

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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

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ALGORITHM SELECTION


• The following predictive algorithms have been used in many research paper

Data Mining

Predictive Algorithms

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TRAINING THE MODEL USING HISTORICAL DATA


• 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

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MODEL(S) TRAINING AND TESTING PHASE


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MODEL(S) ACCURACY


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MODEL(S) ACCURACY CONT.


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APPLYING THE MODEL(S) USING THE NEW DATASET


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APPLYING MODELS USING NEW DATASET


Academic Year 2013-2014

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RESULTS AND FINDINGS


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Winter Balance vs RETAINEDNXTYR (0:No; 1:Yes)


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Winter Living on Campus vs RETAINEDNXTYR (0:No; 1:Yes)


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Winter Received Financial Aid vs RETAINEDNXTYR (0:No; 1:Yes)


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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


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EXAMPLES

Not returning due to the low GPA (0:No; 1:Yes)


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EXAMPLES CONT.

Not returning due to the high balance (0:No; 1:Yes)


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FUTURE WORK


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FUTURE WORK

Attributes for future consideration Student Attendants List Student Credit Hours Repeat Class Indicator Types of Financial Aid Major College Residency Other Attributes


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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


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QUESTIONS & ANSWERS


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THANK YOU!Admir Djulovic, Dennis Wilson


STUDENT RETENTION PREDICTION USING DATA MINING TOOLS AND BANNER DATA Admir Djulovic Dennis Wilson...

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