Laying the basis for the guidance approach, this edition links the well respected and innovative theories about child development and developmentally appropriate practice with principles and practices of guidance for the preschool through primary grade age group.

A GUIDANCE APPROACH FOR THE ENCOURAGING CLASSROOM, 5/E, easily functions as a primary reference for professionals or in classes that address group management, the learning environment, child guidance, child behavior, challenging behavior, conflict management, and peace education topics. The book addresses ages 3-8 years in three parts. Part 1 explores the foundation of guidance in early childhood education and covers key concepts such as conventional discipline versus guidance, mistaken behavior, the guidance tradition, and innovative theories about child development with guidance. Part 2 focuses on building and organizing an encouraging classroom, as well as providing key elements of an encouraging classroom, including daily schedule, routines, use of thematic instruction, importance of working with parents, and leadership communication. Part 3 addresses problem solving and challenging behavior in the encouraging classroom, including a practical illustration for how to use and teach conflict management and coverage of the "five-finger-formula." The book also covers nontraditional families as well as the effects of societal violence in the classroom. Throughout, this experience-based resource includes tips, techniques, and real-life anecdotes that help professionals make the shift from conventional classroom to developmentally appropriate guidance.

The 12 essays collected in this book suggest both practical and theoretical approaches to teaching through networked technologies. Moving beyond technology for its own sake, the book articulates a pedagogy which makes its own productive uses of emergent technologies, both inside and outside the classroom. The book models for students one possible way for teaching and learning the unknown: a dialogic strategy for teaching and learning that can be applied not only to technology-rich problems, but to a range of social issues. This approach, based on the work of Mikhail Bakhtin, understands language itself as a field of creative choices, conflicts, and struggles. After a foreword by Gail E. Hawisher and Cynthia L. Selfe, essays in the book are: (1) "Introduction" (Jeffrey R. Galin and Joan Latchaw); (2) "What Is Seen Depends on How Everybody Is Doing Everything: Using Hypertext To Teach Gertrude Stein's 'Tender Buttons'" (Dene Grigar); (3) "Voices That Let Us Hear: The Tale of the Borges Quest" (Jeffrey R. Galin and Joan Latchaw); (4) "How Much Web Would a Web Course Weave if a Web Course Would Weave Webs?" (Bruce Dobler and Harry Bloomberg); (5) "Don't Lower the River, Raise the Bridge: Preserving Standards by Improving Students' Performances" (Susanmarie Harrington and William Condon); (6) "The Seven Cs of Interactive Design" (Joan Huntley and Joan Latchaw); (7) "Computer-Mediated Communication: Making Nets Work for Writing Instruction" (Fred Kemp); (8) "Writing in the Matrix: Students Tapping the Living Database on the Computer Network" (Michael Day); (9)"Conferencing in the Contact Zone" (Theresa Henley Doerfler and Robert Davis); (10) "Rhetorical Paths and Cyber-Fields: ENFI, Hypertext, and Bakhtin" (Trent Batson); (11) "Four Designs for Electronic Writing Projects" (Tharon W. Howard); and (12) "The Future of Dialogical Teaching: Overcoming the Challenges" (Dawn Rodrigues). A 76-item glossary is attached. (RS)

True family literacy is not just a matter of establishing parent-child book nights and encouraging parents to read aloud to their children. Effective family literacy programs recognize and validate the important ways teachers, families, and community members learn from one another. Family literacy also relies on bringing students' home literacy routines into the classroom then back home again. This important book sheds light on family literacy practices that consider-and celebrate-students' complex and diverse home lives. Cultural considerations are key, and several chapters deal with the need to recognize, respect, and capitalize on home contexts for literacy in order to engage students, families, and communities. In these pages you will Read about successful family literacy programs Gain ideas for incorporating home culture and literacy practices into school settings to better engage students Learn how to effectively communicate literacy practices and goals to parents This volume differs from other books on family literacy in that the contributors themselves reflect the demographic diversity in today's schools. So in addition to presenting their research and classroom experiences, they give rich, personal accounts of their own interactions with students, teachers, and families. And they raise questions about power and access, calling for true learning partnerships. Book jacket.

This book provides the most comprehensive treatment to date of microeconometrics, the analysis of individual-level data on the economic behavior of individuals or firms using regression methods for cross section and panel data. The book is oriented to the practitioner. A basic understanding of the linear regression model with matrix algebra is assumed. The text can be used for a microeconometrics course, typically a second-year economics PhD course; for data-oriented applied microeconometrics field courses; and as a reference work for graduate students and applied researchers who wish to fill in gaps in their toolkit. Distinguishing features of the book include emphasis on nonlinear models and robust inference, simulation-based estimation, and problems of complex survey data. The book makes frequent use of numerical examples based on generated data to illustrate the key models and methods. More substantially, it systematically integrates into the text empirical illustrations based on seven large and exceptionally rich data sets.

Learning Patterns in Higher Education brings together a cutting edge international team of contributors to critically review our current understanding of how students and adults learn, how differences and changes in the way students learn can be measured in a valid and reliable way, and how the quality of student learning may be enhanced. There is substantial evidence that students in higher education have a characteristic way of learning, sometimes called their learning orientation (Biggs 1988), learning style (Evans et al. 2010) or learning pattern (Vermunt and Vermetten 2004). However, recent research in the field of student learning has resulted in multi-faceted and sometimes contradictory results which may reflect conceptual differences and differences in measurement of student learning in each of the studies. This book deals with the need for further clarification of how students learn in higher education in the 21st century and to what extent the measurements often used in learning pattern studies are still up to date or can be advanced with present methodological and statistical insights to capture the most important differences and changes in student learning. The contributions in the book are organized in two parts: a first conceptual and psychological part in which the dimensions of student learning in the 21st century are discussed and a second empirical part in which questions related to how students’ learning can be measured and how it develops are considered. Areas covered include: Cultural influences on learning patterns Predicting learning outcomes Student centred learning environments and self-directed learning Mathematics learning This indispensable book covers multiple conceptual perspectives on how learning patterns can be described and effects and developments can be measured, and will not only be helpful for ‘learning researchers’ as such but also for educational researchers from the broad domain of educational psychology, motivation psychology and instructional sciences, who are interested in student motivation, self-regulated learning, effectiveness of innovative learning environments, as well as assessment and evaluation of student characteristics and learning process variables.

Annotation A straightforward workplace communication system designed to generate brief, results-oriented information exchanged between employees and their managers. The system is easy to learn and implement, and will produce immediate results in the workplace. This concise book includes clear and simple explanations, examples from real workplace case studies, concrete action steps, and brainstorming ideas.

With the advent of computers, very large datasets have become routine. Standard statistical methods don’t have the power or flexibility to analyse these efficiently, and extract the required knowledge. An alternative approach is to summarize a large dataset in such a way that the resulting summary dataset is of a manageable size and yet retains as much of the knowledge in the original dataset as possible. One consequence of this is that the data may no longer be formatted as single values, but be represented by lists, intervals, distributions, etc. The summarized data have their own internal structure, which must be taken into account in any analysis. This text presents a unified account of symbolic data, how they arise, and how they are structured. The reader is introduced to symbolic analytic methods described in the consistent statistical framework required to carry out such a summary and subsequent analysis. Presents a detailed overview of the methods and applications of symbolic data analysis. Includes numerous real examples, taken from a variety of application areas, ranging from health and social sciences, to economics and computing. Features exercises at the end of each chapter, enabling the reader to develop their understanding of the theory. Provides a supplementary website featuring links to download the SODAS software developed exclusively for symbolic data analysis, data sets, and further material. Primarily aimed at statisticians and data analysts, Symbolic Data Analysis is also ideal for scientists working on problems involving large volumes of data from a range of disciplines, including computer science, health and the social sciences. There is also much of use to graduate students of statistical data analysis courses.

Grade level: 1, 2, 3, k, p, e, t.