4 papers
When Offline Selectors Cannot Beat the Best Single Model: A Diagnostic Study on edX Dropout Prediction
Tyler Crosse, Alan Nadelsticher Ruvalcaba, Dustin Khang LeDuc +3
Different predictors often excel on different inputs, so picking the best one per instance promises higher accuracy than committing to a single model. In practice, selectors traine…
A Comparative Analysis of Student Performance Predictions in Online Courses using Heterogeneous Knowledge Graphs
Thomas Trask, Nicholas Lytle, Michael Boyle +2
As online courses become the norm in the higher-education landscape, investigations into student performance between students who take online vs on-campus versions of classes becom…
Ordinal Behavior Classification of Student Online Course Interactions
Thomas Trask
The study in interaction patterns between students in on-campus and MOOC-style online courses has been broadly studied for the last 11 years. Yet there remains a gap in the literat…
Analyzing the Capabilities of Nature-inspired Feature Selection Algorithms in Predicting Student Performance
Thomas Trask
Predicting student performance is key in leveraging effective pre-failure interventions for at-risk students. As educational data grows larger, more effective means of analyzing st…