Improving Equity in Data Science offers a comprehensive look at the ways in which data science can be conceptualized and engaged more equitably within the K-16 classroom setting, moving beyond merely broadening participation in educational opportunities. This book makes the case for field wide definitions, literacies and practices for data science teaching and learning that can be commonly discussed and used, and provides examples from research of these practices and literacies in action.

With a new chapter on the literature review, this accessible step-by-step guide to using the five major approaches to research design is now in a thoroughly revised second edition. The prior edition's user-friendly features are augmented by a new companion website with worksheets keyed to each chapter.

Advancing the Power of Learning Analytics and Big Data in Education provides insights concerning the use of learning analytics, the role and impact of analytics on education, and how learning analytics are designed, employed, and assessed. The chapters will discuss factors affecting learning analytics such as human factors, geographical factors, technological factors, and ethical and legal factors.

The book aims to advance global knowledge and practice in applying data science to transform higher education learning and teaching to improve personalization, access and effectiveness of education for all. Currently, higher education institutions and involved stakeholders can derive multiple benefits from educational data mining and learning analytics by using different data analytics strategies to produce summative, real-time, and predictive or prescriptive insights and recommendations.

Written by two leading experts in education research and policy, Common-Sense Evidence is a concise, accessible guide that helps education leaders find and interpret data and research, and then put that knowledge into action. In the book, Nora Gordon and Carrie Conaway empower educators to address the federal Every Student Succeeds Act mandate that schools use evidence-based improvement strategies.

Data Science in Education Using R is the go-to reference for learning data science in the education field. The book answers questions like: What does a data scientist in education do? How do I get started learning R, the popular open source statistical programming language? And what does a data analysis project in education look like?

Utilizing Educational Data Mining Techniques for Improved Learning: Emerging Research and Opportunities explores data mining and management techniques that promote the improvement and optimization of educational data systems. The book intends to provide new models, platforms, tools, and protocols in data science for educational data analysis and introduces innovative hybrid system models dedicated to data science.