In this study, we used hierarchical linear modeling to examine restricted-use 2011 National Assessment of Educational Progress (NAEP) data to explore relationships between principal-reported time spent on the different NAEP responsibilities of mathematics coaches and specialists (MCSs) and the achievement scores of approximately 37,400…

With the onset and implementation of No Child Left Behind legislation almost two decades ago, coupled with declining performance by U.S. students compared with other developed countries, public education in our country has been under constant scrutiny from all fronts and continues to be there today. Much of the recent emphasis in educational…

Automatic machine learning (AutoML) methods automate the time-consuming, feature-engineering process so that researchers produce accurate student models more quickly and easily. In this paper, we compare two AutoML feature engineering methods in the context of the National Assessment of Educational Progress (NAEP) data mining competition. The…

In high-stakes, large-scale, standardized tests with certain time limits, examinees are likely to engage in either one of the three types of behavior (e.g., van der Linden & Guo, 2008; Wang & Xu, 2015): solution behavior, rapid guessing behavior, and cheating behavior. Oftentimes examinees do not always solve all items due to various…