Exploring the Influence of Academic Background on Student Success in an Online Graduate Degree Program

• In Event: Laying the Groundwork for Meaningful Interactions in Online Learning Environments

Abstract

This study aims to evaluate the contribution of academic background to the prediction of student performance in an online master's degree in analytics. We use machine learning techniques to identify influential parameters in predicting learning performance of online students including their overall GPA and core course grades. The findings from this study demonstrate that some academic history factors such as major during an undergraduate degree program and highest prior degree are significantly related to successful learner outcomes in the online master’s program. Leveraging such machine learning data will help to enhance students’ academic experience and performance, increase pathways to graduation, improve the quality of online teaching, and establish an intelligent course recommendation system.

Authors

• Farahnaz Soleimani, Georgia Institute of Technology
  Non-Presenting Author

• Jeonghyun Lee, Georgia Institute of Technology
  Presenting Author

• Meryem Soylu, Georgia Institute of Technology
  Presenting Author