Susanne P. Lajoie – författare

Highlighting and illustrating several important and interesting theoretical trends that have emerged in the continuing development of instructional technology, this book's organizational framework is based on the notion of two opposing camps. One evolves out of the intelligent tutoring movement, which employs artificial-intelligence technologies in the service of student modeling and precision diagnosis, and the other emerges from a constructivist/developmental perspective that promotes exploration and social interaction, but tends to reject the methods and goals of the student modelers. While the notion of opposing camps tends to create an artificial rift between groups of researchers, it represents a conceptual distinction that is inherently more interesting and informative than the relatively meaningless divide often drawn between "intelligent" and "unintelligent" instructional systems. An evident trend is that researchers in both "camps" view their computer learning environments as "cognitive tools" that can enhance learning, performance, and understanding. Cognitive tools are objects provided by the instructional environment that allow students to incorporate new auxiliary methods or symbols into their social problem solving which otherwise would be unavailable. A final section of the book represents researchers who are assimilating and accommodating the wisdom and creativity of their neighbors from both camps, perhaps forming the look of technology for the future. When the idea of model tracing in a computer-based environment is combined with appreciation for creative mind-extension cognitive tools and for how a community of learners can facilitate learning, a camp is created where AI technologists and social constructivist learning theorists can feel equally at home.

Highlighting and illustrating several important and interesting theoretical trends that have emerged in the continuing development of instructional technology, this book's organizational framework is based on the notion of two opposing camps. One evolves out of the intelligent tutoring movement, which employs artificial-intelligence technologies in the service of student modeling and precision diagnosis, and the other emerges from a constructivist/developmental perspective that promotes exploration and social interaction, but tends to reject the methods and goals of the student modelers. While the notion of opposing camps tends to create an artificial rift between groups of researchers, it represents a conceptual distinction that is inherently more interesting and informative than the relatively meaningless divide often drawn between "intelligent" and "unintelligent" instructional systems. An evident trend is that researchers in both "camps" view their computer learning environments as "cognitive tools" that can enhance learning, performance, and understanding. Cognitive tools are objects provided by the instructional environment that allow students to incorporate new auxiliary methods or symbols into their social problem solving which otherwise would be unavailable. A final section of the book represents researchers who are assimilating and accommodating the wisdom and creativity of their neighbors from both camps, perhaps forming the look of technology for the future. When the idea of model tracing in a computer-based environment is combined with appreciation for creative mind-extension cognitive tools and for how a community of learners can facilitate learning, a camp is created where AI technologists and social constructivist learning theorists can feel equally at home.

An issue in the current push for reform in mathematics education is the call to address statistics at the precollege level. This volume represents the emerging findings of an interdisciplinary collaboration among a group of mathematics educators, cognitive scientists, teachers, and statisticians to construct an understanding of how to introduce statistics education and assessment for students in elementary and secondary schools. A premise shared by the contributors to this volume is that when students are introduced to statistics at the K-12 level and provided with opportunities to do statistics that are related to actual life situations, they will be better prepared for decision making in the real world. The interdisciplinary nature of the group of researchers stimulated a lively interchange of ideas for enhancing the learning, teaching, and assessment of statistical understanding, which is reflected in this volume. Mathematics educators contribute their insights into how teachers teach mathematical ideas and heighten our awareness of the ecological needs of the current mathematics classroom. Cognitive scientists share their understanding of developmental differences in learning and present theoretical perspectives that contribute to the design of effective learning environments. Classroom teachers share their ideas about classroom activities and assessment of student learning, as well as their concerns for in-service training and workshops to help teachers acquire skills in this new content area. Statisticians offer their understanding of what is feasible to teach in the early grades, and what their view is of statistical literacy. The book is organized around four interdependent themes: content, teaching, learning, and assessment. By focusing their respective chapters on particular themes, the authors intend to cultivate a better understanding of how each relates to improvements in statistics education. This is the first book to: * address statistics learning in grades K-12, * address issues of statistical curriculum content in grades K-12, * address issues of assessment of statistics learning in grades K-12, * bring issues of technology instruction and assessment in statistics education in grades K-12, and * look at teacher education for statistics instruction in grades K-12. This is a must-read book for both practitioners and researchers involved in K-12 mathematics education.

An issue in the current push for reform in mathematics education is the call to address statistics at the precollege level. This volume represents the emerging findings of an interdisciplinary collaboration among a group of mathematics educators, cognitive scientists, teachers, and statisticians to construct an understanding of how to introduce statistics education and assessment for students in elementary and secondary schools. A premise shared by the contributors to this volume is that when students are introduced to statistics at the K-12 level and provided with opportunities to do statistics that are related to actual life situations, they will be better prepared for decision making in the real world. The interdisciplinary nature of the group of researchers stimulated a lively interchange of ideas for enhancing the learning, teaching, and assessment of statistical understanding, which is reflected in this volume. Mathematics educators contribute their insights into how teachers teach mathematical ideas and heighten our awareness of the ecological needs of the current mathematics classroom. Cognitive scientists share their understanding of developmental differences in learning and present theoretical perspectives that contribute to the design of effective learning environments. Classroom teachers share their ideas about classroom activities and assessment of student learning, as well as their concerns for in-service training and workshops to help teachers acquire skills in this new content area. Statisticians offer their understanding of what is feasible to teach in the early grades, and what their view is of statistical literacy. The book is organized around four interdependent themes: content, teaching, learning, and assessment. By focusing their respective chapters on particular themes, the authors intend to cultivate a better understanding of how each relates to improvements in statistics education. This is the first book to: * address statistics learning in grades K-12, * address issues of statistical curriculum content in grades K-12, * address issues of assessment of statistics learning in grades K-12, * bring issues of technology instruction and assessment in statistics education in grades K-12, and * look at teacher education for statistics instruction in grades K-12. This is a must-read book for both practitioners and researchers involved in K-12 mathematics education.

Since the publication of the first edition of Computers as Cognitive Tools in 1993, rapid changes have taken place in the uses of technology for educational purposes and in the theories underlying such uses. Changes in perspectives on thinking and learning are guiding the instructional design of computer-based learning environments. Computers as Cognitive Tools, Volume II: No More Walls provides examples of state-of-the-art technology-based research in the field of education and training. These examples are theory-driven and reflect the learning paradigms that are currently in use in cognitive science. The learning theories, which consider the nature of individual learning, as well as how knowledge is constructed in social situations, include information processing, constructivism, and situativity. Contributors to this volume demonstrate some variability in their choice of guiding learning paradigms. This allows readers the opportunity to examine how such paradigms are operationalized and validated. An array of instructional and assessment approaches are described, along with new techniques for automating the design and assessment process. New considerations are offered as possibilities for examining learning in distributed situations. A multitude of subject matter areas are covered, including scientific reasoning and inquiry in biology, physics, medicine, electricity, teacher education, programming, and hypermedia composition in the social sciences and ecology. This volume reconsiders the initial "camp" analogy posited in 1993 edition of Computers as Cognitive Tools, and presents a mechanism for breaking camp to find new summits.

Since the publication of the first edition of Computers as Cognitive Tools in 1993, rapid changes have taken place in the uses of technology for educational purposes and in the theories underlying such uses. Changes in perspectives on thinking and learning are guiding the instructional design of computer-based learning environments. Computers as Cognitive Tools, Volume II: No More Walls provides examples of state-of-the-art technology-based research in the field of education and training. These examples are theory-driven and reflect the learning paradigms that are currently in use in cognitive science. The learning theories, which consider the nature of individual learning, as well as how knowledge is constructed in social situations, include information processing, constructivism, and situativity. Contributors to this volume demonstrate some variability in their choice of guiding learning paradigms. This allows readers the opportunity to examine how such paradigms are operationalized and validated. An array of instructional and assessment approaches are described, along with new techniques for automating the design and assessment process. New considerations are offered as possibilities for examining learning in distributed situations. A multitude of subject matter areas are covered, including scientific reasoning and inquiry in biology, physics, medicine, electricity, teacher education, programming, and hypermedia composition in the social sciences and ecology. This volume reconsiders the initial "camp" analogy posited in 1993 edition of Computers as Cognitive Tools, and presents a mechanism for breaking camp to find new summits.

Richard Snow's research influenced many students and colleagues, both directly through his findings and indirectly by inspiring others to carry on the work. A cross-section of his influence is represented in this special issue. The articles present several themes in his work, including the importance of multivariate considerations of individual differences, adapting instruction to individual learners, a process understanding of aptitude, and an enlarged role for spatial ability. Each paper picks up one of the themes identified--trait complexes, ATIs, process analyses, and spatial ability--and has a strong quantitative, empirical foundation but is nested within appropriately complex theoretical frameworks.