John W. Lloyd

Barbara Bateman

Melody Tankersley

Enduring Issues in Special Education is aimed at any course in the undergraduate or graduate special education curriculum that is wholly or partly devoted to a critical examination of current issues in special education. The book organizes 28 chapters into seven sections using familiar structuring principles—what, who, where, how, when, why, and whither. Each section begins with an introduction that provides historical, legal, and theoretical background information and organizing commentary for the chapters that follow. The book’s objective, in addition to informing readers about the issues, is to develop critical thinking skills in the context of special education. Key features include the following: Dialectic Format – Each of the 28 chapters presents compelling reasons for addressing the issue at hand and specific ways to do so. Because each issue is written from different perspectives and focuses on a variety of aspects, readers are encouraged to weigh the arguments, seek additional information, and come up with synthesized positions of their own. Organizing Framework – The book’s seven sections have been arranged according to a scheme that is the essence of most investigative reporting and provides a coherent, easy-to-understand framework for readers. Expertise – All chapters are written by leading scholars who are highly regarded experts in their fields and conclude with suggested readings and discussion questions for additional study.

Personal Perspectives

Enduring Issues In Special Education

This book provides a systematic approach to knowledge representation, computation, and learning using higher-order logic. For those interested in computational logic, it provides a framework for knowledge representation and computation based on higher-order logic, and demonstrates its advantages over more standard approaches based on first-order logic. For those interested in machine learning, the book explains how higher-order logic provides suitable knowledge representation formalisms and hypothesis languages for machine learning applications.

John W. Lloyd

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