Students often enter higher education academically unprepared and with unrealistic perceptions and expectations of university life, which are critical factors that influence students’ decisions to leave their institutions prior to degree completion. Advances in educational technology and the current availability of vast amounts of educational data make it possible to represent how students interact with higher education resources, as well as provide insights into students’ learning behavior and processes. This volume offers new research in such learning analytics and demonstrates how they support students at institutions of higher education by offering personalized and adaptive support of their learning journey. It focuses on four major areas of discussion: · Theoretical perspectives linking learning analytics and study success. · Technological innovations for supporting student learning. · Issues and challenges for implementing learning analytics at higher education institutions. · Case studies showcasing successfully implemented learning analytics strategies at higher education institutions. Utilizing Learning Analytics to Support Study Success ably exemplifies how educational data and innovative digital technologies contribute to successful learning and teaching scenarios and provides critical insight to researchers, graduate students, teachers, and administrators in the general areas of education, educational psychology, academic and organizational development, and instructional technology.

This popular guide offers a wealth of innovative, research-based strategies for making K–12 classrooms the best learning environments they can be. Easy-to-implement best practices are presented for establishing a classroom management plan, organizing procedures and materials, building a respectful community, fostering peer collaboration, and engaging students in interactive learning. Each of the 50 strategies includes step-by-step instructions, the amount of time needed to implement, and the recommended grade level. In a large-size format for easy photocopying, the book features ready-to-use reproducibles. New to This Edition *Now in a large, easy-to-reproduce format. *Strategies for using technology to promote interactive learning. *Explains a gradual release-of-responsibility model of instruction to support self-motivated learning. *Additional topics: vocabulary development, enhancing writing skills, and building peer partnerships. *Updated and expanded research base.

Data. Does the word make you cringe? Does it evoke feelings of guilt? Are you unsure how to distill it and use it effectively? Grab this book and learn how to empower yourself and your school community with information gleaned from your school's data. Experienced educators and authors offer simple instructions that can help focus school improvement efforts and result in increasing teacher expertise--a factor that positively affects the quality of life for students long after they have left the classroom. Accepting responsibility for such far-reaching influence requires educators to adopt instructional improvement as a standard by which a school needs to operate and as a means to collaborate and interact with one another. More than that, though, instructional improvement is an important component of successful schools. Learn how to improve instruction by * Collecting the right data--the right way. * Incorporating relevant data into everyone's daily life. * Resisting the impulse to set brand-new goals every year. * Never settling for "good enough." * Anticipating changes--big and small, local and federal. * Collaborating and avoiding privatized practice. * Involving all stakeholders in identifying problems, setting goals, and analyzing data. * Agreeing on what constitutes high-quality instruction and feedback. The challenge is to understand that data--not intuition or anecdotal reports--are tools to be used in getting better at teaching students. And teaching students effectively is what schools are all about. Following the guidance in this book, overcome uncertainty and concerns about data as you learn to collect and analyze both soft and hard data and use their secrets for instructional improvement in your school.

Violence and the threat of violence in schools are legitimate concerns for researchers and educators wishing to create safe and productive learning environments for all students. 'Appraisal and Prediction of School Violence: Methods, Issues, and Contexts', edited by Michael Furlong, Michael Bates, Douglas Smith, and Paul Kingery, provides up-to-date information about best practices for understanding, predicting, and preventing violence on school campuses. In addition to providing in-depth coverage of approaches to assessing threats of violence in schools, the book covers important related topics such as bullying in schools, anger and hostility among students, discipline strategies, weapon possession, crisis preparation and response, and diversity and disability issues related to school safety. The book is essential reading for researchers, school administrators, counselors, psychologists, and other school personnel who are working to reduce the incidence of school violence in a manner that supports positive student development.

Data Analysis for Continuous School Improvement provides a new definition of school improvement, away from a singular focus on compliance, toward a true commitment to excellence. This book is a call to action. It is about inspiring schools and school districts to commit to continuous school improvement by providing a framework that will result in improving teaching for every teacher and learning for every student through the comprehensive use of data. A culmination of over 30 years of doing the hard work in schools and districts both nationally and internationally, Data Analysis for Continuous School Improvement shares new, evidence-based learnings about how to analyze, report, communicate, and use multiple measures of data. The updated edition provides a wealth of tools, protocols, timelines, examples, and strategies that will help schools and districts become genuine learning organizations.

Domino Effect: Its Prediction and Prevention, Volume Five in the Methods in Chemical Process Safety series, focuses on the process of learning from experience, including elements of process safety management, human factors in the chemical process industries, and the regulation of chemical process safety, including current approaches. Users will find this book to be an informative tool and user manual for process safety for a variety of professionals. This new release focuses on Domino effect – Case histories and accident statistics, the state-of-the-art in domino effect modeling, Fire Driven Domino Effect, Mitigation of Domino Effect, and much more. - Acquaints readers/researchers with the fundamentals of process safety - Provides the most recent advancements and contributions from a practical point-of-view - Gives readers the views/opinions of experts on each topic

Minimize problem behavior and maximize student success! Acting-out behavior by students manifests in ways that make classroom management and teaching very challenging. Building on a model using seven phases of acting-out behavior presented in the first edition, the newly updated edition draws on new research in applied behavior analysis, sound instructional principles, and functional behavior assessment to deliver a clear roadmap for educators to design interventions in a clear, systematic, and achievable matter. Features include: Managing each phase of the acting-out cycle—from structuring the classroom, to handling escalated behavior, to recovery Case studies that distill concrete action steps from the book’s concepts Checklists, tools, resources, and templates for applying the book’s principles to any classroom

Hydraulic Excavators are subjected to tip-over situations with potential hazardous outcome. The given work analyses the state of the art of tip-over protection methods, including the current mandatory overload protection system. Using methods derived from the area of robotics, an algorithm for use as an assistance system was developed to predict and actively prevent dangerous tip-over situations. The developed method was evaluated in multi-body simulation models and in physical field tests.