For more than 25 years, the pioneering research and theories of Norbert Seel have had a profound impact on educational thought in mathematics. In this special tribute, an international panel of researchers present the current state of model-based education: its research, methodology, and technology. Fifteen stimulating, sometimes playful chapters link the multiple ways of constructing knowledge (and domains as diverse as cognitive science, computer science, and philosophy) to the complex real world of skill development; generalize model-based theories into educational settings; and explain how to design and evaluate model-centered learning environments. Extensive reading lists, provocative graphics, and a wealth of cultural touchstones from the Bible to Bob Dylan make Understanding Models for Learning and Instruction an accessible yet thought provoking collection.

A sampling of the topics covered:

This synthesis of latest innovations and fresh perspectives on classic constructs makes Understanding Models for Learning and Instruction cutting-edge reading for the researchers and educators in mathematics instruction building the next generation of educational models.

What is knowledge? How can it be successfully assessed? How can we best use the results? As questions such as these continue to be discussed and the learning sciences continue to deal with expanding amounts of data, the challenge of applying theory to diagnostic methods takes on more complexity.

Computer-Based Diagnostics and Systematic Analysis of Knowledge meets this challenge head-on as an international panel of experts reviews current and emerging assessment methodologies in the psychological and educational arenas. Emphasizing utility, effectiveness, and ease of interpretation, contributors critically discuss practical innovations and intriguing possibilities (including mental representations, automated knowledge visualization, modeling, and computer-based feedback) across fields ranging from mathematics education to medicine. These contents themselves model the steps of systematic inquiry, from theoretical construct to real-world application:

Researchers and professionals in education psychology, instructional technology, computer science, and linguistics will find Computer-Based Diagnostics and Systematic Analysis of Knowledge a stimulating guide to a complex present and a rapidly evolving future.

This volume explores the rapidly advancing field of technology-supported knowledge assessment. Across academia, research on learning and instruction, AI-based analysis, psychology, and education, there is a pressing need for a comprehensive collection of foundations and methodologies related to knowledge. While the market offers books on individual and locally developed methods, a holistic overview is currently lacking. It aims to fill that gap, inspiring projects globally and benefiting knowledge-intensive developments in both digital and traditional learning environments. Understanding the state and processes of knowledge often poses a bottleneck in the quality of designs and implementations. This book addresses this challenge by focusing on mostly automated, easy-to-implement strategies, supporting the crucial task of understanding knowledge.

For more than 25 years, the pioneering research and theories of Norbert Seel have had a profound impact on educational thought in mathematics. In this special tribute, an international panel of researchers present the current state of model-based education: its research, methodology, and technology. Fifteen stimulating, sometimes playful chapters link the multiple ways of constructing knowledge (and domains as diverse as cognitive science, computer science, and philosophy) to the complex real world of skill development; generalize model-based theories into educational settings; and explain how to design and evaluate model-centered learning environments. Extensive reading lists, provocative graphics, and a wealth of cultural touchstones from the Bible to Bob Dylan make Understanding Models for Learning and Instruction an accessible yet thought provoking collection.

A sampling of the topics covered:

This synthesis of latest innovations and fresh perspectives on classic constructs makes Understanding Models for Learning and Instruction cutting-edge reading for the researchers and educators in mathematics instruction building the next generation of educational models.

What is knowledge? How can it be successfully assessed? How can we best use the results? As questions such as these continue to be discussed and the learning sciences continue to deal with expanding amounts of data, the challenge of applying theory to diagnostic methods takes on more complexity.

Computer-Based Diagnostics and Systematic Analysis of Knowledge meets this challenge head-on as an international panel of experts reviews current and emerging assessment methodologies in the psychological and educational arenas. Emphasizing utility, effectiveness, and ease of interpretation, contributors critically discuss practical innovations and intriguing possibilities (including mental representations, automated knowledge visualization, modeling, and computer-based feedback) across fields ranging from mathematics education to medicine. These contents themselves model the steps of systematic inquiry, from theoretical construct to real-world application:

Researchers and professionals in education psychology, instructional technology, computer science, and linguistics will find Computer-Based Diagnostics and Systematic Analysis of Knowledge a stimulating guide to a complex present and a rapidly evolving future.

This volume explores the rapidly advancing field of technology-supported knowledge assessment. Across academia, research on learning and instruction, AI-based analysis, psychology, and education, there is a pressing need for a comprehensive collection of foundations and methodologies related to knowledge. While the market offers books on individual and locally developed methods, a holistic overview is currently lacking. It aims to fill that gap, inspiring projects globally and benefiting knowledge-intensive developments in both digital and traditional learning environments. Understanding the state and processes of knowledge often poses a bottleneck in the quality of designs and implementations. This book addresses this challenge by focusing on mostly automated, easy-to-implement strategies, supporting the crucial task of understanding knowledge.