In today 's rapidly changing and information-rich world, students are not acquiring adequate knowledge and skills to prepare them for careers in mathematics, science, and technology with the traditional approach to assessment and instruction. New competencies (e.g., information communication and technology skills) are needed to deal successfully with the deluge of data. In order to accomplish this, new "educationally valuable" skills must be acknowledged and assessed. Toward this end, the skills we value and support for a society producing knowledge workers, not simply service workers, must be identified, together with methods for their measurement.
Innovative Assessment for the 21st Century explores the faces of future assessment and ask hard questions, such as: What would an assessment that captures all of the above attributes look like? Should it be standardized? What is the role of the professional teacher?

Useful to researchers as well as practitioners looking for guidance on designing automated instruction systems, this book provides a snapshot of the state-of-the-art in this research area. In so doing, it focuses on the two critical problems: first, diagnosis of the student's current level of understanding or performance; and second, selection of the appropriate intervention that will transition the student toward expert performance. Containing a comprehensive set of principled approaches to automated instruction, diagnosis, and remediation, it is the first volume on the topic to provide specific, detailed guidance on how to develop these systems. Leading researchers and practitioners represented in this book address the following questions in each chapter: * What is your approach to cognitive diagnosis for automated instruction? * What is the theoretical basis of your approach? * What data support the utility of the approach? * What is the range of applicability of your approach? * What knowledge engineering or task analysis methods are required to support your approach? Referring to automated instruction as instruction that is delivered on any microprocessor-based system, the contributors to -- and editors of -- this book believe that is it possible for automated instructional systems to be more effective than they currently are. Specifically, they argue that by using artificial intelligence programming techniques, it is possible for automated instructional systems to emulate the desirable properties of human tutors in one-on-one instruction.

Useful to researchers as well as practitioners looking for guidance on designing automated instruction systems, this book provides a snapshot of the state-of-the-art in this research area. In so doing, it focuses on the two critical problems: first, diagnosis of the student's current level of understanding or performance; and second, selection of the appropriate intervention that will transition the student toward expert performance. Containing a comprehensive set of principled approaches to automated instruction, diagnosis, and remediation, it is the first volume on the topic to provide specific, detailed guidance on how to develop these systems. Leading researchers and practitioners represented in this book address the following questions in each chapter: * What is your approach to cognitive diagnosis for automated instruction? * What is the theoretical basis of your approach? * What data support the utility of the approach? * What is the range of applicability of your approach? * What knowledge engineering or task analysis methods are required to support your approach? Referring to automated instruction as instruction that is delivered on any microprocessor-based system, the contributors to -- and editors of -- this book believe that is it possible for automated instructional systems to be more effective than they currently are. Specifically, they argue that by using artificial intelligence programming techniques, it is possible for automated instructional systems to emulate the desirable properties of human tutors in one-on-one instruction.

In today's rapidly changing and information-rich world, students are not acquiring adequate knowledge and skills to prepare them for careers in mathematics, science, and technology with the traditional approach to assessment and instruction. New competencies (e.g., information communication and technology skills) are needed to deal successfully with the deluge of data. In order to accomplish this, new "educationally valuable" skills must be acknowledged and assessed. Toward this end, the skills we value and support for a society producing knowledge workers, not simply service workers, must be identified, together with methods for their measurement. Innovative Assessment for the 21st Century explores the faces of future assessment-and ask hard questions, such as: What would an assessment that captures all of the above attributes look like? Should it be standardized? What is the role of the professional teacher?

The first International Conference on Intelligent Tutoring Systems (ITS) was held ten years ago in Montreal (ITS '88). It was so well received by the international community that the organizers decided to do it again in Montreal four years later, in 1992, and then again in 1996. ITS '98 differs from the previous ones in that this is the first time the conference has been held outside of Montreal, and it's only been two years (not four) since the last one. One interesting aspect of the ITS conferences is that they are not explicitly bound to some organization (e.g., IEEE or AACE). Rather, the founder of these conferences, Claude Frasson, started them as a means to congregate researchers actively involved in the ITS field and provide a forum for presentation and debate of the most currently challenging issues. Thus the unifying theme is science. This year's "hot topics" differ from those in the earlier ITS conferences as they reflect ever changing trends in ITS research. A few of the issues being examined at ITS '98 include: Web based tutoring systems, deploying ITS in the real world, tutoring and authoring tools, architectures, and knowledge structure and representation.