As part of its Partnership for Pre-K Improvement (PPI) initiative, the Bill & Melinda Gates Foundation sponsored the RAND Corporation to study the cost of high-quality pre-K programming. The RAND study included three states--Oregon, Tennessee, and Washington--that were partnering with the foundation under PPI. The objective of the study was to…

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…

There is strong evidence that children who attend high-quality pre-kindergarten (pre-K) programs learn skills that benefit them in school and life. The price tag of most private programs puts them out of reach for some families, however. In response, many states and school districts fund pre-K programs to expand opportunities for their youngest…

States and localities throughout the United States are expanding their investments in pre-kindergarten (pre-K) programs. Although spending on publicly funded pre-K programs is well documented, relatively less is known about the true cost to deliver the programs, especially considering varying quality standards and accounting for the resources used…

Institutional staff such as enrollment managers, business officers, and institutional researchers are often asked to predict enrollments. Developing any predictive model can be intimidating, particularly when there is no textbook to follow. This paper provides a practical framework for generating enrollment projection options and for evaluating…

In this ITEMS module, we provide a two-part introduction to the topic of reliability from the perspective of "classical test theory" (CTT). In the first part, which is directed primarily at beginning learners, we review and build on the content presented in the original didactic ITEMS article by Traub and Rowley (1991). Specifically, we…

Researchers have explored a variety of topics related to identifying and distinguishing among specific types of rater effects, as well as the implications of different types of incomplete data collection designs for rater-mediated assessments. In this study, we used simulated data to examine the sensitivity of latent trait model indicators of…

The Big Data for Education Spoke of the NSF Northeast Big Data Innovation Hub and ETS co-sponsored an educational data mining competition in which contestants were asked to predict efficient time use on the NAEP 8th grade mathematics computer-based assessment, based on the log file of a student's actions on a prior portion of the assessment. In…

Consistency of learning behaviors is known to play an important role in learners' engagement in a course and impact their learning outcomes. Despite significant advances in the area of learning analytics (LA) in measuring various self-regulated learning behaviors, using LA to measure consistency of online course engagement patterns remains largely…

When a new student comes to play an educational game, how can we determine what content to give them such that they learn as much as possible? When a frustrated customer calls in to a helpline, how can we determine what to say to best assist them? When an ill patient comes in to the clinic, how do we determine what tests to run and treatments to…

The development in information science has enabled an explosive growth of data, which attracts more and more researchers to engage in the field of big data analytics. Noticeably, in many real-world applications, large amounts of data are imbalanced data since the events of interests occur infrequently. Classification of imbalanced data is an…

User clustering algorithms have been introduced to analyze users' learning behaviors and help to provide personalized learning guides in traditional Web-based learning systems. However, the explicit and implicit coupled interactions, which means the correlations between user attributes generated from learning actions, are not considered in these…

Missing data is endemic in much educational research. However, practices such as step-wise regression common in the educational research literature have been shown to be dangerous when significant data are missing, and multiple imputation (MI) is generally recommended by statisticians. In this paper, we provide a review of these advances and their…

The proliferation of digital information technologies and related infrastructure has given rise to novel ways of capturing, storing and analyzing data. In this paper, we describe the research and development of an information system called Interactive Knowledge Networks for Engineering Education Research (iKNEER). This system utilizes a framework…

Missing data are a common occurrence in survey-based research studies in education, and the way missing values are handled can significantly affect the results of analyses based on such data. Despite known problems with performance of some missing data handling methods, such as mean imputation, many researchers in education continue to use those…