Relying on a series of empirical workplace studies as well as an extensive review of psychological, sociological and educational literature, the authors develop a framework for examining human competence as a process of networked expertise. Networked expertise refers to competencies that arise from social interaction, knowledge sharing, and collective problem solving. These are embedded in communities and organized groups of experts and professionals. Cognition and intelligent activity are not only individual and mental processes but ones which rely on socio-culturally developed cognitive tools. These include physical and conceptual artifacts as well as socially distributed and shared processes of intelligent activity embedded in complex social and cultural environments. Networked expertise is relational in nature. It is constituted in interaction between individuals, communities, and larger networks supported by cognitive artifacts, and it coevolves with continuously transforming innovative knowledge communities.

The focus of the book is on analyzing the socio-cognitive foundations of human intelligent activity. The authors examine theories and models that help to understand individual and social aspects of processes of learning, development of expertise, knowledge creation, and innovation. These processes are studied both in the contexts of education and work, and are illuminated with numerous examples, and interview data. The main topics covered are the development of expertise, distributed cognition and shared expertise, collaborative and cultural learning, and inquiry-based and computer-supported learning processes. The basic tenet of the book is that knowledge sharingshould be a core value in all organizations. This is the first step of answering to the challenges of emerging knowledge society.

Order affects the results you get: Different orders of presenting material can lead to qualitatively and quantitatively different learning outcomes. These differences occur in both natural and artificial learning systems. In Order to Learn shows how order effects are crucial in human learning, instructional design, machine learning, and both symbolic and connectionist cognitive models. Each chapter explains a different aspect of how the order in which material is presented can strongly influence what is learned by humans and theoretical models of learning in a variety of domains. In addition to data, models are provided that predict and describe order effects and analyze how and when they will occur. The introductory and concluding chapters compile suggestions for improving learning through better sequences of learning materials, including how to take advantage of order effects that encourage learning and how to avoid order effects that discourage learning. Each chapter also highlights questions that may inspire further research. Taken together, these chapters show how order effects in different areas can and do inform each other. In Order to Learn will be of interest to researchers and students in cognitive science, education, machine learning.