In modeling missing data, the missing data latent variable of the confirmatory factor model accounts for systematic variation associated with missing data so that replacement of what is missing is not required. This study aimed at extending the modeling missing data approach to tetrachoric correlations as input and at exploring the consequences of…

Recurrence quantification analysis (RQA) is a time-series analysis method that uses autocorrelation properties of typing data to detect regularities within the writing process. The following paper first gives a detailed introduction to RQA and its application to time series data. We then apply RQA to keystroke logging data of first and foreign…

This dissertation was prepared to determine if a correlation existed between a student's poverty level and an educational diagnosis of serious emotional disturbance. I hypothesize that students at or under the United States' definition of poverty are more likely to exhibit signs and symptoms of a serious emotional disturbance and therefore be…

The multidimensional mixture data structure exists in many test (or inventory) conditions. Heterogeneity also relatively exists in populations. Still, some researchers are interested in deciding to which subpopulation a participant belongs according to the participant's factor pattern. Thus, in this study, we proposed three analysis procedures…

Computer-mediated social learning contexts have become increasingly popular over the last few years; yet existing models of students' cognitive-affective states have been slower to adopt dyadic interaction data for predictions. Here, we explore the possibility of capitalizing on the inherently social component of collaborative learning by using…

Amidst ongoing technological and social change, this article explores the implications for critical education that result from a data-driven model of digital governance. The article argues that traditional notions of critique which rely upon the deconstruction and analysis of texts are increasingly redundant in the age of datafication, where the…

Poor academic performance remains among the most concerning educational issues, especially in higher education and online learning. To address the concern, institutions like the University of Southeastern Philippines (USeP) leverage educational data mining (EDM) techniques to generate relevant information from learning management systems (LMS)…

The Stanford Education Data Archive (SEDA) launched in 2016 to provide nationally comparable, publicly available test score data for U.S. public school districts. I introduce a special collection of six articles that each use SEDA to lend their questions and findings a national scope. Together, these articles demonstrate a range of uses of SEDA…

Many institutions in the field of education have been involved in distance education with the learning management system. In this context, there has been a rapid increase in data in the e-learning process as a result of the development of technology and the widespread use of the internet. This increase is in the size of large data. Today, big data…

Measures of association, which typically require pairwise data, are widespread in many aspects of educational research. However, due to the need to reduce their content to equal numbers of units of analysis, they are rarely found in the analysis of textbooks. In this paper, we present two methods for overcoming this limitation, one through the use…

Purpose: The analysis of Ecological Momentary Assessment (EMA) data can be difficult to conceptualize due to the complexity of how the data are collected. The goal of this tutorial is to provide an overview of statistical considerations for analyzing observational data arising from EMA studies. Method: EMA data are collected in a variety of ways,…

Effect sizes are important because they are an accessible way to indicate the practical importance of observed associations or differences. Standardized mean difference (SMD) effect sizes, such as Cohen's d, are widely used in education and the social sciences -- in part because they are relatively easy to calculate. However, SMD effect sizes…

It is of critical importance to be aware of the historical discrimination embedded in the data and to consider a fairness measure to reduce bias throughout the predictive modeling pipeline. Given various notions of fairness defined in the literature, investigating the correlation and interaction among metrics is vital for addressing unfairness.…

Higher education institutions in the United States are under intense scrutiny to engage in data-driven decision-making to address systemic deficiencies. An innovative strategy to increase the use of data in higher education is through interactive data visualization whereby users are more involved in the creation of knowledge. The problem that was…

The purpose of this study is to derive a correlation between the technological thinking disposition and the computational thinking ability of gifted students in Korea. The correlation between each element was analyzed by looking at the sub-elements of computational thinking according to the components of technological thinking disposition. The…