Using Machine Learning Techniques in Student Dropout Prediction

Author(s): Rajeev Bukralia (Dakota State University, USA), Amit V. Deokar (Dakota State University, USA), Surendra Sarnikar (Dakota State University, USA)and Mark Hawkes (Dakota State University, USA)
Copyright: 2012
Pages: 15
EISBN13: 9781466609907

Purchase

View Using Machine Learning Techniques in Student Dropout Prediction on the publisher's website for pricing and purchasing information.

View Sample PDF

Abstract

This chapter outlines a case of identifying students at-risk of dropping out of online courses by using institutional research data. The case delineates this step-by-step process that includes identification of appropriate constructs and variables, data collection, data pre-processing, data analysis, and model evaluation to develop a predictive model for student dropout in online courses at a small, public, Midwest university in the United States. Included is a comparative data analysis of various machine learning techniques, such as Artificial Neural Networks (ANN), Decision Trees, and Support Vector Machines (SVM), with statistical Logistic Regression (LR) analysis. The chapter provides steps for data analysis and predictive modeling using the open source, downloadable data mining software, WEKA. The chapter concludes with a discussion on the challenges and suggestions for building a predictive model in the context of institutional research.

The IRMA Community

Research IRM

Using Machine Learning Techniques in Student Dropout Prediction

Purchase

Abstract

Related Content

IRMA Sponsors