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…

Researchers have expressed concern about "implementation" fidelity in intervention research but have not extended that concern to "assessment" fidelity, or the extent to which pre-/posttests are administered and interpreted as intended. When studying reading interventions, data gathering heavily influences the identification of…

Evaluators use their eyes to see what is there, whether it is intended or not. But they use their test instruments to measure what is intended, whether it is there or not. Evaluators have been broadening their repertoire of instruments for years: curriculum-embedded tests, observer checklists, audiotape recorders, videotape recorders, unobtrusive…