Predicting STEM Student Retention Based on Engagement Features and ePortfolio Analysis
As providers of higher education begin to harness the power of big data analytics, one very fitting application for these new techniques is that of predicting student attrition. The ability to pinpoint students who might soon decide to drop out of a given academic program allows those in charge to not only understand the causes for this undesired outcome, but it also provides room for the development of early intervention systems. While making such inferences based on academic performance data alone is certainly possible, we claim that in many cases there is no substantial correlation between how well a student performs and his or her decision to withdraw. This is specially true when the overall set of students has a relatively similar academic performance. To address this issue, this project aims at deriving measurements of engagement from students’ electronic portfolios and using these features to augment the quality of predictions, while also tackling imbalanced data and other problems.