Differences in Student Trajectories via Filtered Time Series Analysis in an Immersive Virtual World

To scaffold students' investigations of an inquiry-based immersive virtual world for science education without undercutting the affordances an open-ended activity provides, this study explores ways time-stamped log files of groups' actions may enable the automatic generation of formative supports. Groups' logged actions in the virtual world are filtered via principal component analysis to provide a time series trajectory showing the rate of their investigative activities over time. This technique functions well in open-ended environments and examines the entire course of their experience in the virtual world instead of specific subsequences. Groups' trajectories are grouped via k-means clustering to identify different typical pathways taken through the immersive virtual world. These different approaches are then correlated with learning gains across several survey constructs (affective dimensions, ecosystem science content, understanding of causality, and experimental methods) to see how various trends are associated with different outcomes. Differences by teacher and school are explored to see how best to support inclusion and success of a diverse array of learners.