The current period in the history of mankind has been coined as the knowledge age (Brown & Duguid, 2000; Bereiter, 2002). This term serves to distinguish this period from its predecessor, the information age. In contrast to information, knowledge entails a knower, is hard to detach from its owner, and seems to be something that we digest rather than hold. Knowledge lies less in databases than in people, and has to be disclosed by some form of collective activity, and people have to be learn how be engaged in collaborative activities that produce new knowledge. In professional contexts at least, the people who construct knowledge are called 'knowledge workers', a term that can be associated with slavery, under those who coordinate them, and who need knowledge for economic reasons. Because knowledge does not really have ownership, it can be turned into economic value by anyone that knows how to do it. Whatever the undertone, currently there is a more than humanitarian interest in collaborative learning, especially in forms of collaboration that allow people to display and develop their knowledge.The information age was a label indicating a period characterised by rapid developments in information technology, initially inspired by a naive belief that computers, as storage and retrieval devices, could meet all demands for relevant information, and thereby solve most of our learning problems. Now we know better, but what we still do not fully understand is how to construct knowledge technology (Roschelle & Pea, 2002). On the one hand, we suppose that knowledge is highly situational rather than general, and activity bound rather than a product to hold. This calls for specific solutions in the form of tools that support situational activities. On the other hand, collaboration is still taken as a general skill, so people can do it, or they cannot. This calls for the opposite type of solution: general, and content free tools, that can be implemented in many collaborative situations. For us as scientists, the concept of knowledge worker is interesting, because it requires us to think about how to integrate conceptions about knowledge with those of collaborative work, especially in the context of new technologies, without detaching the activities from the individuals and their situation.

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Four architectures for computer supported collaborative learning systems are analyzed using the model-view-controller design pattern and compared from the standpoints of coupling between activities of the users and suitability for educational use, as well as network load and ease of implementation. The architectures are illustrated with examples from the developmental history of Belvedere, an environment for collaborative construction of knowledge representations during problem solving. A hybrid architecture that supports model-level coupling is shown to provide the best design tradeoffs.

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Increasingly, social and collaborative processes are seen as central to the development of thinking skills, including the skills of scientific thinking. With colleagues we have developed a software system (Belvedere) to support high-school science students in engaging in collaborative reasoning and argumentation. We discuss how Belvedere is designed to support students' collaborative processes, describe our formative evaluation studies, and provide examples of student sessions. Reflecting on students' inter-actions with Belvedere and each other, we then discuss the competencies and limitations of students in developing scientific argumentation and reasoning, as distinguished from and supported by their practice of everyday argumentation..

Analyzing a conference, especially one as young and focused as LAK, provides the opportunity to observe the structure and contributions of the scientific community around it. This work will perform a Scientometric analysis, coupled with a more in-depth manual content analysis, to extract this insight from the proceedings and program of LAK 2013. Authorship analysis reveals an open and international community, while internal citation analysis provides evidence of the construction of a body of knowledge central to Learning Analytics. The analysis of the content of the papers found five main topics: visualization, behaviour analysis, social learning analytics, learning analytics for MOOCs, and learning analytics issues (ethical, scalability, etc.), as well as papers reflecting on the field itself. We discuss representative papers presented at the conference, highlighting trends and new developments. Learning analytics is a diverse multidisciplinary field with an emerging interdisciplinary core, well situated to benefit from productive dialogue concerning its scope and purpose and to reflect on the pedagogies and epistemologies implied by its methods.