Uncovering Latent Profiles Based on How Students Review Paper-Based Assessments

Blended learning environments offer a rich amount of data that encompasses various learning interactions. Despite advancements in technology, there have been several learning activities that remain offline, thus preventing us from fully understanding certain aspects of student learning. An example of this is how students review their paper-based assessments. Using a homegrown educational technology that addresses this gap, we analyzed students' clickstream data to uncover latent profiles based on how they review these graded assessments. Such behavior could provide insight into their self-regulated learning strategies as they attempt to correct their misconceptions. We leveraged latent profile analysis and presented our preliminary findings and interpretations of the five student profiles we uncovered to understand the effects of their varying efforts to learn the course material.