This study highlights the role and importance of advanced, machine learning-driven predictive models in enhancing the accuracy and timeliness of identifying students at-risk of negative academic outcomes in data-driven Early Warning Systems (EWS). K-12 school districts have, at best, 13 years to prepare students for adulthood and success. They…

Monitoring applications (MAs) use digital and online tools to collect and track data on student behavior, and they have become increasingly popular among schools. Empirical research on these complex surveillance platforms is scant, and little is known about the efficacy or impact that they have on students. This study used a multi-method…

Technology-based assessments that involve collaboration among students offer many sources of process data, although it remains unclear which aspects of these data are most meaningful for making inferences about students' collaborative skills. Recent research has focused mainly on theory-based rubrics for qualitative coding of process data (e.g.,…