This study conducted secondary analysis of data from the National Longitudinal Transition Study-2 (NLTS2) to examine the degree to which student, family, and school constructs predicted self-determination outcomes. Multi-group structural equation modeling was used to examine predictive relationships between 5 students, 4 family, and 7 school…

Hierarchical organization of schooling in all nations insures that international large-scale assessment data are multilevel where students are nested within schools and schools are nested within nations. Longitudinal follow-up of these students adds an additional level. Hierarchical or multilevel models are appropriate to analyze such data. A…

This Innovative Practice Category Work In Progress paper presents an application of machine learning and data mining to student performance data in an undergraduate electrical engineering program. We are developing an analytical approach to enhance retention in the program especially among underrepresented groups. Our approach will provide…

In this analysis of extant data from 3,011 schools implementing school-wide positive behavioral interventions and supports (SWPBIS) across multiple years, we assessed the predictive power of various school characteristics and speed of initial implementation on sustained fidelity of implementation of SWPBIS at 1, 3, and 5 years. In addition, we…

We examined the age of onset, over-time dynamics, and mechanisms underlying science achievement gaps in U.S. elementary and middle schools. To do so, we estimated multilevel growth models that included as predictors children's own general knowledge, reading and mathematics achievement, behavioral self-regulation, sociodemographics, other child-…

This chapter assesses the potential for several prominent technological innovations to promote equality of educational opportunities. We review the history of technological innovations in education and describe several prominent innovations, including intelligent tutoring, blended learning, and virtual schooling.

Research suggests that the academic achievement of deaf and hard-of-hearing (DHH) students is the result of a complex interplay of many factors. These factors include characteristics of the students (e.g., hearing thresholds, language fluencies, mode of communication, and communication functioning), characteristics of their family environments…