Early childhood is a crucial stage in a child's life, and aspects of the environment in the physical, social-emotional, cognitive, and health and safety domains all play important roles in shaping children's development during these early years. Having a valid and reliable measure of the quality of these aspects of children's care settings is…

As information literacy and data-driven program evaluations have become growing obsessions within academic institutions, how might liberal arts and humanities programmes engage students in both critical and creative explorations of contemporary human-data relations? Inspired by the variety of metaphors that currently shape human-data relations and…

Longitudinal data analysis has received widespread interest throughout educational, behavioral, and social science research, with latent growth curve modeling currently being one of the most popular methods of analysis. Despite the popularity of latent growth curve modeling, limited attention has been directed toward understanding the issues of…

The timing of events (e.g., how long it takes a child to exhibit a particular behavior) is often of interest in developmental science. Multilevel survival analysis (MSA) is useful for examining behavioral timing in observational studies (i.e., video recordings) of children's behavior. We illustrate how MSA can be used to answer 2 types of research…

Each year, policymakers, educators, and the general public engage in debates around the statewide average teacher pay figure that the North Carolina Department of Public Instruction (DPI) publishes on its website and reports to the National Education Association. These debates take up the important consideration of whether or not teachers are…

Recent advances in computational linguistics and the social sciences have created new opportunities for the education research community to analyze relevant large-scale text data. However, the take-up of these advances in education research is still nascent. In this paper, we review the recent automated text methods relevant to educational…

Autism spectrum disorder (ASD) research has yet to leverage "big data" on the same scale as other fields; however, advancements in easy, affordable data collection and analysis may soon make this a reality. Indeed, there has been a notable increase in research literature evaluating the effectiveness of machine learning for diagnosing…

The purpose of this study is to examine the abstracts in 6th International Preschool Education Congress abstract book in terms of research subject, method, model, sample type, data collection tools, data analysis techniques, validity and reliability. This study is a qualitative study and was conducted using document analysis, which is one of the…

Purpose: Data mining algorithms using electronic health records (EHRs) are useful in large-scale population-wide studies to classify etiology and comorbidities (Casey et al., 2016). Here, we apply this approach to developmental language disorder (DLD), a prevalent communication disorder whose risk factors and epidemiology remain largely…

As institutions seek to shift into more advanced analytics and data-based decision-support, many institutional research offices face the challenge of meeting the office's current demands while taking on more intricate and specialized work to support decision-making. Given the great need organizations have for information that supports real-time…

Recently, the promotion of science, technology, engineering and mathematics (STEM) education has become the highlight due to the shortage in the STEM workforce. Surprisingly, the enrolment rates in STEM degrees are still low in many countries. Social media has been identified as one of the main platforms that can help to increase prospective…

Education systems produce a large number of valuable data for all stakeholders. The processing of these educational data and making studies on the future of education based on the data reveal highly meaningful results. In this study, an insight was tried to be developed on the educational data collected from ninth-grade students by using data…

Constructing scientific arguments is an important practice for students because it helps them to make sense of data using scientific knowledge and within the conceptual and experimental boundaries of an investigation. In this study, we used a text mining method called Latent Dirichlet Allocation (LDA) to identify underlying patterns in students…

"Doing economics" is an important theme of undergraduate economics programs. Capstone courses increasingly include instruction in "data literacy" and the STEM-related skills of quantitative and empirical methods. Because the professional discipline has moved in this direction and because of greater employer demand for these…

The purpose of this article is to share how one teacher education program at a Midwestern university prepares teacher candidates to analyze student achievement data, which is believed essential as one of the roles a teacher candidate needs in order to develop teacher leadership in the classroom. The authors provide the teacher candidates' relevant…