Clustering, a prevalent class of machine learning (ML) algorithms used in data mining and pattern-finding--has increasingly helped engineering education researchers and educators see and understand assessment patterns at scale. However, a challenge remains to make ML-enabled educational inferences that are useful and reliable for research or…

This study explores a variety of variables with the intent of identifying specific student groups that may struggle with performance in a large general education course. The ultimate objective of this study is to facilitate the success of acknowledged at-risk students. Drawing in part on the theory of social capital, this study examines…

The purpose of this study was to examine how small groups of students in an introductory undergraduate chemistry course construct CERs and the influence of race, gender, and perceived status during this construction. The small groups of students engaged in three chemistry activities that required them to generate a claim, evidence, and reasoning…

Economically disadvantaged (ECD) students come to classrooms performing below grade level, particularly in reading. The purpose of this qualitative case study was to examine master educators' identities and practices that contribute to success in literacy development with ECD students. The performance of ECD students compared to more affluent…

Given the rising prevalence rates of autism spectrum disorders (ASD), schools are serving an increasing number of students with ASD (Scull & Winkler, 2011). Researchers have highlighted active engagement as a critical component of effective interventions for students with ASD (National Research Council, 2001), yet there is limited research…