Educators are increasingly responsible for using data to improve teaching and learning in their schools. This helpful guide provides leaders with simple steps for facilitating accurate analysis and interpretation of data, while avoiding common errors and pitfalls. How to Make Data Work provides clear strategies for getting data into workable shape and creating an environment that supports understanding, analysis, and successful use of data, no matter what data system or educational technology tools are in place in your district. This accessible resource makes data easy to understand and use so that educators can better evaluate and maximize their systems to help their staff, students, and school succeed. With this tried-and-true guidance, you’ll be prepared to advocate for tools that adhere to data reporting standards, avoid misinterpretation of data, and improve the data use climate in your school.

This book is devoted to the Educational Data Mining arena. It highlights works that show relevant proposals, developments, and achievements that shape trends and inspire future research. After a rigorous revision process sixteen manuscripts were accepted and organized into four parts as follows: · Profile: The first part embraces three chapters oriented to: 1) describe the nature of educational data mining (EDM); 2) describe how to pre-process raw data to facilitate data mining (DM); 3) explain how EDM supports government policies to enhance education. · Student modeling: The second part contains five chapters concerned with: 4) explore the factors having an impact on the student's academic success; 5) detect student's personality and behaviors in an educational game; 6) predict students performance to adjust content and strategies; 7) identify students who will most benefit from tutor support; 8) hypothesize the student answer correctness based on eye metrics and mouse click. · Assessment: The third part has four chapters related to: 9) analyze the coherence of student research proposals; 10) automatically generate tests based on competences; 11) recognize students activities and visualize these activities for being presented to teachers; 12) find the most dependent test items in students response data. · Trends: The fourth part encompasses four chapters about how to: 13) mine text for assessing students productions and supporting teachers; 14) scan student comments by statistical and text mining techniques; 15) sketch a social network analysis (SNA) to discover student behavior profiles and depict models about their collaboration; 16) evaluate the structure of interactions between the students in social networks. This volume will be a source of interest to researchers, practitioners, professors, and postgraduate students aimed at updating their knowledge and find targets for future work in the field of educational data mining.

Numerical evidence is everywhere and how best to handle and leverage it is a growing concern in the academic world in general and the academic library world in particular. Libraries are not only storehouses and key contacts for library patrons in accessing numbers, but are also collectors and users of their own data, which is integral to the functioning of the library itself. The essays in Library Data: Empowering Practice and Persuasion focus on interpreting and using library-generated and outside data in support of data-driven practice and data-strengthened persuasion. The collection includes such topics as how to make data presentations appealing and effective; applying capital-budgeting models to libraries; and using data for evaluation and improvement of collections and services. Articles also cover specialized scenarios, including reference, collection development, serial acquisitions, institutional repositories, web site design, interlibrary loan, and bibliographic instruction.