This groundbreaking report provides examples of best practices in value-added modelling for measuring school performance.

This groundbreaking report provides examples of best practices in value-added modelling for measuring school performance.

This collection presents educational assessment research from Latin America, adding to a relatively small but growing body of research considering educational assessment and evaluation issues in this large region. The predominance of Chile reflects its early highly centralized education system, and the fact that it adopted national testing before other Latin American countries. It was also an early participant in international assessment programmes. Other countries have followed the trend of implementing national testing, and to a lesser extent participating in international surveys. The complementary development of technical expertise in quantitative research methods has enabled extensive analysis of the large data sets generated by these testing and assessment programmes. Taken together, the evidence reported provides a means not only of reviewing educational quality issues in Latin America, but also of facilitating comparisons that allow the context specificity of equivalent research conducted in western developed countries to be considered. The chapters in this book were originally published as a special issue of Assessment in Education: Principles, Policy and Practice.

The abundance of data and the rise of new quantitative and statistical techniques have created a promising area: data analytics. This combination of a culture of data-driven decision making and techniques to include domain knowledge allows organizations to exploit big data analytics in their evaluation and decision processes. Also, in education and learning, big data analytics is being used to enhance the learning process, to evaluate efficiency, to improve feedback, and to enrich the learning experience. As every step a student takes in the online world can be traced, analyzed, and used, there are plenty of opportunities to improve the learning process of students. First, data analytics techniques can be used to enhance the student’ s learning process by providing real-time feedback, or by enriching the learning experience. Second, data analytics can be used to support the instructor or teacher. Using data analytics, the instructor can better trace, and take targeted actions to improve, the learning process of the student. Third, there are possibilities in using data analytics to measure the performance of instructors. Finally, for policy makers, it is often unclear how schools use their available resources to "produce" outcomes. By combining structured and unstructured data from various sources, data analytics might provide a solution for governments that aim to monitor the performance of schools more closely. Data analytics in education should not be the domain of a single discipline. Economists should discuss the possibilities, issues, and normative questions with a multidisciplinary team of pedagogists, philosophers, computer scientists, and sociologists. By bringing together various disciplines, a more comprehensive answer can be formulated to the challenges ahead. This book starts this discussion by highlighting some economic perspectives on the use of data analytics in education. The book begins a rich, multidisciplinary discussion that may make data analytics in education seem as natural as a teacher in front of a classroom.

This review report for the Netherlands provides, from an international perspective, an independent analysis of major issues facing the Dutch evaluation and assessment framework in education, current policy initiatives and possible future approaches.

Value-added methods refer to efforts to estimate the relative contributions of specific teachers, schools, or programs to student test performance. In recent years, these methods have attracted considerable attention because of their potential applicability for educational accountability, teacher pay-for-performance systems, school and teacher improvement, program evaluation, and research. Value-added methods involve complex statistical models applied to test data of varying quality. Accordingly, there are many technical challenges to ascertaining the degree to which the output of these models provides the desired estimates. Despite a substantial amount of research over the last decade and a half, overcoming these challenges has proven to be very difficult, and many questions remain unanswered-at a time when there is strong interest in implementing value-added models in a variety of settings. The National Research Council and the National Academy of Education held a workshop, summarized in this volume, to help identify areas of emerging consensus and areas of disagreement regarding appropriate uses of value-added methods, in an effort to provide research-based guidance to policy makers who are facing decisions about whether to proceed in this direction.