Although big data applications in education have different usage areas, they can make important contributions to the evaluation of universities and education systems, decision-making and strategy development, and improvement of the education system. In this study, which is considered to contribute to the academicians who will work on big data in…

Randomized experiments involving education interventions are typically implemented as cluster randomized trials, with schools serving as clusters. To design such a study, it is critical to understand the degree to which learning outcomes vary between versus within clusters (schools), specifically the intraclass correlation coefficient. It is also…

Researchers often assess causal effects of educational programs or policies using educational assessment data. This dissertation explores novel methods of estimating causal effects in educational assessment data and is broken into three parts. The first part proposes a regression discontinuity design with an ordinal running variable to assess the…

The European Education Area aims to support Member States' efforts in enhancing the educational attainment of younger generations. In this policy context, there is a need for an objective tool to assess the educational outcomes of EU countries. The present report addresses this need by pursuing two objectives: (1) providing a comprehensive method…

The "Information Systems Education Journal" has published uninterrupted since 2003. Over its publication history, it has covered myriad topics related to information systems education including model curriculum, outcomes assessment, online learning, capstone courses, service learning, data analytics, and cybersecurity, just to name a…

Analysis of user-generated data (for example process data from logfiles, learning analytics, and data mining) in computer-based environments has gained much attention in the last decade and is considered a promising evolving field in learning sciences. In the area of educational assessment, the benefits of such data and how to exploit them are…

This book provides a comprehensive introduction by an extraordinary range of experts to the recent and rapidly developing field of learning analytics. Some of the finest current thinkers about ways to interpret and benefit from the increasing amount of evidence from learners' experiences have taken time to explain their methods, describe examples,…

There is a severe shortage of Black scientists in the United States. Amid the recent national movement to dismantle systemic racism and racial injustices, many scientists publicly highlighted the prevalence of racist learning environments in STEM, contributing to the underrepresentation of Black students in STEM fields. Acknowledging this…

Educational data science (EDS) is an emerging, interdisciplinary research domain that seeks to improve educational assessment, teaching, and student learning through data analytics. Teachers have been portrayed in the EDS literature as users of pre-constructed data dashboards in educational technologies, with little consideration given to them as…

On the long and arduous journey toward effective educational assessment, business schools have progressed in their ability to clearly state measurable learning goals and use direct measures of student learning. However, many schools are wrestling with the last stages of the journey--measuring present learning outcomes, implementing…

The Stanford Educational Data Archive (SEDA) is the first data set to allow comparisons of district academic achievement and growth from Grades 3 to 8 across the United States, shining a light on the distribution of educational opportunities. This study describes a convergent validity analysis of the SEDA growth estimates in mathematics and…

IES is the primary research, evaluation, and statistics arm of the U.S. Department of Education. Established through the Education Sciences Reform Act of 2002 (ESRA), the Institute's mission is to expand fundamental knowledge and understanding of education and to provide education leaders and practitioners, parents and students, researchers, and…

Learning Analytics is an emerging research field and design discipline that occupies the "middle space" between the learning sciences/educational research and the use of computational techniques to capture and analyze data (Suthers & Verbert, 2013). We propose that the literature examining the triadic relationships between…

A core goal for most learning analytic projects is to move from small-scale research towards broader institutional implementation, but this introduces a new set of challenges because institutions are stable systems, resistant to change. To avoid failure and maximize success, implementation of learning analytics at scale requires explicit and…

Aggregate survey responses collected from students are commonly used by universities to compare effective educational practices across program majors, and to make high-stakes decisions about the effectiveness of programs. Yet if there is too much heterogeneity among student responses within programs, the program-level averages may not…