Big data is a unique field of study which requires specialized analytics. The field of education has a lot of data: individual student test scores, attendance, behavior, and demographic data are just some of the regularly collected information year after year. Individual student data across an entire state over several years quickly becomes big…

The Powerful Learning with Computational Thinking report explains how the Digital Promise team works with districts, schools, and teachers to make computational thinking ideas more concrete to practitioners for teaching, design, and assessment. We describe three powerful ways of using computers that integrate well with academic subject matter and…

The Colorado Growth Model (CGM) was developed jointly by the Colorado Department of Education (CDE), the Technical Advisory Panel for Longitudinal Growth (TAP), and the National Center for the Improvement of Educational Assessment (NCIEA). Its development was required by state statute (SB09-163) and assigned to the Technical Advisory Panel. The…

This article characterizes and reflects on the possible uses of early warning systems (hereafter, EWS) in the region as effective tools to support educational pathways, whenever they identify risks of dropout, difficulties for the achievement of substantive learning, and the possibility of organizing specific actions. This article was developed in…

Computer science concepts have an important part in other subjects and thinking computationally is being recognized as an important skill for everyone, which leads to the increasing interest in developing computational thinking (CT) as early as at the comprehensive school level. Therefore, research is needed to have a common understanding of CT…

Multidimensional item response theory (MIRT) models have generated increasing interest in the psychometrics literature. Efficient approaches for estimating MIRT models with dichotomous responses have been developed, but constructing an equally efficient and robust algorithm for polytomous models has received limited attention. To address this gap,…

We examine the challenges of teaching probability through the use of modelling. We argue how an integrated modelling approach might facilitate a coordinated understanding of distribution by marrying theoretical and data-oriented perspectives and present probability as more connected to the social lives of modern-day students. Research is, however,…

Cyberinfrastructure (CI) empowers the foundational computation resources underlying data analytics, spatial modeling, and many other domains serving the growing knowledge economy in the United States. In every part of these interactions with CI, questions of how to seamlessly integrate CI training into educational programs exist. In this article,…

The relationship between public education expenditures and student outcomes remains an important concern for policy analysts, educational administrators, and the public at large. While previous studies have failed to identify a consistent relationship between public investments in education and positive student outcomes, most analyses have not…

Current teacher preparation programs have received scrutiny for failing to sufficiently support teachers in valuing and developing their knowledge of data literacy. The current study explores a collaborative approach to increasing pre-service teachers' experiences with data literacy by including under-utilized data experts: school psychologists.…

This article reports on a rigorous approach developed for calibrating the Evidence-Based Adequacy Model to suit the Ontario K-12 public education context, and the actual calibrations made. The four-step calibration methodology draws from expert consultations and a review of the academic literature. Specific attention is given to the technical…

Student assessment nonparticipation (or opt out) has increased substantially in K-12 schools in states across the country. This increase in opt out has the potential to impact achievement and growth (or value-added) measures used for educator and institutional accountability. In this simulation study, we investigated the extent to which…

Data mining has been successfully used by financial and retail companies since the mid-1960's to create predictive models and reveal unexpected relationships. However, it remains underutilized as a tool in educational research. Large-scale standardized assessment programs such as the Trends in International Mathematics and Science Study (TIMSS)…

Determining which students are at risk of poorer outcomes -- such as dropping out, failing classes, or decreasing standardized examination scores -- has become an important area of research and practice in both K-12 and higher education. The detectors produced from this type of predictive modeling research are increasingly used in early warning…

Item response models typically assume that the item characteristic (step) curves follow a logistic or normal cumulative distribution function, which are strictly monotone functions of person test ability. Such assumptions can be overly-restrictive for real item response data. We propose a simple and more flexible Bayesian nonparametric IRT model…