This study compares two missing data procedures in the context of ordinal factor analysis models: pairwise deletion (PD; the default setting in Mplus) and multiple imputation (MI). We examine which procedure demonstrates parameter estimates and model fit indices closer to those of complete data. The performance of PD and MI are compared under a…

As a digital learning medium, serious games can be powerful, immersive educational vehicles and provide large data streams for understanding player behavior. Educational data mining and learning analytics can effectively leverage big data in this context to heighten insight into student trajectories and behavior profiles. In application of these…

Realist evaluation (RE) is a research design increasingly used in program evaluation, that aims to explore and understand the influence of context and underlying mechanisms on intervention or program outcomes. Several methodological challenges, however, are associated with this approach. This article summarizes RE key principles and examines some…

In this article I respond to the tendency of the law to approach 'the public interest' as a legal test, thereby drawing the criticism that this narrow notion of what purports to be in the "public" interest is wholly disconnected from the views of actual publics, and lacks social legitimacy. On the other hand, to simply extrapolate…

In data collected from virtual learning environments (VLEs), item response theory (IRT) models can be used to guide the ongoing measurement of student ability. However, such applications of IRT rely on unbiased item parameter estimates associated with test items in the VLE. Without formal piloting of the items, one can expect a large amount of…

Binary item response theory (IRT) models are widely used in educational testing data. These models are not perfect because they simplify the individual item responding process, ignore the differences among different response patterns, cannot handle multidimensionality that lay behind options within a single item, and cannot manage missing response…

The long-term goal of the State Systemic Improvement Plan (SSIP) and other federal and state early intervention and early childhood education initiatives is improved child and family outcomes. States play a critical role in supporting practitioners in the use of evidence-based practices to improve child and family outcomes. When practitioners…

Students often struggle to interpret graphs correctly, despite emphasis on graphic literacy in U.S. education standards documents. The purpose of this study was to describe challenges sixth graders with varying levels of science and mathematics achievement encounter when transitioning from interpreting graphs having discrete independent variables…

This chapter identifies some of the challenges surrounding data collection and analysis regarding development students, including testing and identifying academic needs, placement determination, and measuring student outcomes.

This report presents school-level finance data on expenditures by function from the School-Level Finance Survey (SLFS). The SLFS is an extension of two existing collections being conducted by the National Center for Education Statistics (NCES) in collaboration with the Census Bureau: the School District Finance Survey (F-33) and the state-level…

This practice guide is an updated version of "Supporting and Responding to Behavior: Evidence-based Classroom Strategies for Teachers" (see ED619696) that replaces, rather than supplements, the first version. This guide summarizes evidence-based, positive, and proactive practices that support and respond to students' social, emotional,…

This case study describes an experience of using constructivist grounded theory to analyze data. The project investigated how high school students improved their approaches to learning mathematics. Over 4 months, students participated in processes which supported their learning while simultaneously generating data, including interactive writing,…

The recent change in the general social survey (GSS) to a rotating panel design is a landmark development for social scientists. Sociological methodologists have argued that fixed-effects (FE) models are generally the best starting point for analyzing panel data because they allow analysts to control for unobserved time-constant heterogeneity. We…

The data revolution has given citizens access to enormous large-scale open databases. In order to take into account the full complexity of data, we have to change the way we think in terms of the nature of data and its availability, the ways in which it is displayed and used, and the skills that are required for its interpretation. Substantial…

This paper investigates the intersection of big data and philosophy of education by considering big data's potential for addressing learning via a holistic process of coming-to-know. Learning, in this sense, cannot be reduced to the difference between a pre- and post-test, for example, as it is constituted at least as much by qualities of…