Measuring race and ethnicity for administrative data sets and then analyzing these data to understand racial/ethnic disparities present many logistical and theoretical challenges. In this chapter, we conduct a synthetic review of studies on how to effectively measure race/ethnicity for administrative data purposes and then utilize these measures…

In recent years there has been a proliferation of massive open online courses (MOOCs), which provide unprecedented opportunities for lifelong learning. Registrants approach these courses with a variety of motivations for participation. Characterizing the different types of participation in MOOCs is fundamental in order to be able to better…

At the end of the first decade of the 21st century, the rapid growth in the MOOCs offering brought about a new educational landscape, posing new challenges to teaching and learning, mainly due to massive participation, ubiquity and free enrollment. These courses embody a confluence of technological and pedagogical mediations yet to be fully…

The state of Wisconsin has one of the highest four year graduation rates in the nation, but deep disparities among student subgroups remain. To address this the state has created the Wisconsin Dropout Early Warning System (DEWS), a predictive model of student dropout risk for students in grades six through nine. The Wisconsin DEWS is in use…

Predicting the success or failure of a student in a course or program is a problem that has recently been addressed using data mining techniques. In this paper we evaluate some of the most popular classification and regression algorithms on this problem. We address two problems: prediction of approval/failure and prediction of grade. The former is…

A deluge of empirical research became available on MOOCs in 2013-2015 and this research is available in disparate sources. This paper addresses a number of gaps in the scholarly understanding of MOOCs and presents a comprehensive picture of the literature by examining the geographic distribution, publication outlets, citations, data collection and…

This paper reports on a study to predict students at risk of failing based on data available prior to commencement of first year. The study was conducted over three years, 2010 to 2012, on a student population from a range of academic disciplines, n=1,207. Data was gathered from both student enrollment data and an online, self-reporting,…

In light of the emergence of statistical analysis techniques based on data mining in education sciences, and the potential they offer to detect non-trivial information in large databases, this paper presents a procedure used to detect factors linked to academic achievement in large-scale assessments. The study is based on a non-experimental,…

This study reviews a selected sample of 274 research articles on ELT, published between 2005 and 2015 in Turkish contexts. In the study, 15 journals in ULAKBIM database and articles from national and international journals accessed according to convenience sampling method were surveyed and relevant articles were obtained. A content analysis was…

This paper presents a content analytic approach on doctoral dissertations in the field of distance education in Turkish Higher Education context from the years of 1986 through 2014. A total of 61 dissertations were examined to explore keywords, academic discipline, research areas, theoretical/conceptual frameworks, research designs, research…

Racial classification is a paramount concern in data collection and analysis for American Indians and Alaska Natives (AI/ANs) and has far-reaching implications in health research. We examine how different racial classifications affect survey weights and consequently change health-related indicators for the AI/AN population in California. Using a…

Surveillance video recording is becoming ubiquitous in daily life for public areas such as supermarkets, banks, and airports. The rate at which surveillance video is being generated has accelerated demand for machine understanding to enable better content-based search capabilities. Analyzing human activity is one of the key tasks to understand and…

A new line of research has emerged that examines the prevalence rates of mixed methods within disciplines in the social/behavioral sciences. Research presented in this article is unique in that it examines prevalence rates across multiple disciplines using an established cross-disciplinary classification scheme. Results indicate that there are…

This report examines career/technical education (CTE) coursetaking of 2005 public high school graduates using a recently revised taxonomy and new indicators of participation to present a more contemporary and detailed picture of CTE coursetaking than is provided by previously-used indicators. To understand how students use the CTE curriculum, this…

In this paper, we present a data-based user modeling framework that uses both unsupervised and supervised classification to build student models for exploratory learning environments. We apply the framework to build student models for two different learning environments and using two different data sources (logged interface and eye-tracking data).…