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Comparing Data Mining Models in Academic Analytics

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  • Dheeraj Raju

    (School of Nursing, University of Alabama at Birmingham, Birmingham, AL, USA)

  • Randall Schumacker

    (College of Education, The University of Alabama, Tuscaloosa, AL, USA)

Abstract

The goal of this research study was to compare data mining techniques in predicting student graduation. The data included demographics, high school, ACT profile, and college indicators from 1995-2005 for first-time, full-time freshman students with a six year graduation timeline for a flagship university in the south east United States. The results indicated no difference in misclassification rates between logistic regression, decision tree, neural network, and random forest models. The results from the study suggest that institutional researchers should build and compare different data mining models and choose the best one based on its advantages. The results can be used to predict students at risk and help these students graduate.

Suggested Citation

  • Dheeraj Raju & Randall Schumacker, 2016. "Comparing Data Mining Models in Academic Analytics," International Journal of Knowledge-Based Organizations (IJKBO), IGI Global, vol. 6(2), pages 38-54, April.
  • Handle: RePEc:igg:jkbo00:v:6:y:2016:i:2:p:38-54
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