Computer-supported collaborative learning (CSCL) is often based on written argumentative discourse of learners, who discuss their perspectives on a problem with the goal to acquire knowledge. Lately, CSCL research focuses on the facilitation of specific processes of argumentative knowledge construction, e.g., with computer-supported collaboration scripts. In order to refine process-oriented instructional support, such as scripts, we need to measure the influence of scripts on specific processes of argumentative knowledge construction. In this article, we propose a multi-dimensional approach to analyze argumentative knowledge construction in CSCL from sampling and segmentation of the discourse corpora to the analysis of four process dimensions (participation, epistemic, argumentative, social mode).

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A framework to analyze argumentative knowledge construction in computer-supported collaborative learning

Many people think that arguing interferes with learning. They link argumentation to a
certain type of oppositional argument that is increasingly prevalent in our media culture. Tannen
(1998) analyzed the aggressive types of argument that are frequently seen on talk shows and in
the political sphere, where representatives of two opposed viewpoints spout talking points at
each other. In these forms of argumentation, the goal is not to work together toward a common
position, but simply to score points. All teachers and parents have seen children engaged in this
type of argumentation, and most would probably agree that it has little to contribute to education.

Arguing to learn

Knowledge in Pieces (KiP) is a distinctive epistemological perspective on conceptual change, employing a “modeling” approach. Its theory consists mainly of several detailed and empirically consequential models of different kinds of knowledge, including both intuitive “preconceptions” and normative knowledge. This chapter offers a brief introduction to KiP centering on two core models, illustrating how they enfold multiple time-scales-from details of real-time learning to multi-year accomplishments-and how they accommodate empirically salient diversity in the way different students learn.

Knowledge in Pieces

This paper presents a framework for security requirements elicitation and analysis. The framework is based on constructing a context for the system, representing security requirements as constraints, and developing satisfaction arguments for the security requirements. The system context is described using a problem-oriented notation, then is validated against the security requirements through construction of a satisfaction argument. The satisfaction argument consists of two parts: a formal argument that the system can meet its security requirements and a structured informal argument supporting the assumptions expressed in the formal argument. The construction of the satisfaction argument may fail, revealing either that the security requirement cannot be satisfied in the context or that the context does not contain sufficient information to develop the argument. In this case, designers and architects are asked to provide additional design information to resolve the problems. We evaluate the framework by applying it to a security requirements analysis within an air traffic control technology evaluation project.

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Security Requirements Engineering: A Framework for Representation and Analysis

This paper reports a study that attempts to explore how using mobile technologies in direct physical interaction with space and with other players can be combined with principles of engagement and self-motivation to create a powerful and engaging learning experience We developed a mobile gaming experience designed to encourage the development of children's conceptual understanding of animal behaviour Ten children (five boys and five girls) aged between 11 and 12 years played and explored the game The findings from this study offer interesting insights into the extent to which mobile gaming might be employed as a tool for supporting learning It also highlights a number of major challenges that this format raises for the organisation of learning within schools and the design of such resources

Savannah: mobile gaming and learning?

About the book: Computer Supported Argument Visualization is attracting attention across education, science, public policy and business. More than ever, we need sense-making tools to help negotiate understanding in the face of multi-stakeholder, ill-structured problems. In order to be effective, these tools must support human cognitive and discursive processes, and provide suitable representations, services and user interfaces.

Visualizing Argumentation is written by practitioners and researchers for colleagues working in collaborative knowledge media, educational technology and organizational sense-making. It will also be of interest to theorists interested in software tools which embody different argumentation models. Particular emphasis is placed on the usability and effectiveness of tools in different contexts.

Visualizing Argumentation: Software Tools for Collaborative and Educational Sense-Making

Kirschner, P., Carr, Ch., Van Merrienboer, J., Sloep, P. (2003). How Expert Designers Design: Priorities in Designing Competence-based Learning. Performance Improvement Quarterly. 15, 86-104.

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How Expert Designers Design

In this article we study how groups of beginning experts solve a type of problem that has been referred to as ‘wicked’ or ‘social science problem’. An example of such a problem is that of school drop-out, which can be defined as a school and curriculum problem, a socioeconomic problem, a cultural problem, a behavioral problem, et cetera. Mono-disciplinary teams of beginning experts in different fields addressed the problem of advising the State Board of Governors on measures to reduce school drop-out. One group demonstrated expertlike behavior in solving the problem. The other groups concentrated on particular aspects of the problem and its proposed solution whilst neglecting other aspects. Their approach contained a mixture of (beginning) expert and novice behavior, demonstrating that the expertnovice continuum is not clear-cut for these types of problem. The use of external representations that signal which aspects of the problem solving process need additional effort is discussed as a promising support, in particular to inter-disciplinary teams. Expertise used in solving wicked problems 3 Use of Expertise by Beginning Experts Solving Wicked Problems The kind of problem dealt with here has been referred to as ‘wicked’ in the literature on planning and design (Kunz & Rittel, 1970). Such problems are often typified in psychological literature as ill-structured and more specific as ‘social science problems’ (Voss, Greene, Post, & Penner, 1983; Voss, Tyler, & Yengo, 1983). Solving this type of problem is elusive because it often is not clear what area of expertise is (most) relevant to solving it. Consider the example problem reported on in this article: What can you advise the Board of Governors to help reduce high school drop-out? Drop-out itself can be approached as an educational problem, but it also makes sense to consider it a social problem, or an economic problem, and so forth. These approaches are related to criteria by which interventions to reduce drop-out may be evaluated. Moreover, other criteria may have to be considered as well, such as political feasibility and social and moral acceptability. Voss studied this type of problem, which he called ‘social science problems’, using expert-novice paradigms (Voss, Green et al., 1983; Voss, Tyler et al., 1983) and his classic example problem, is that of how to increase the crop production in the Soviet Union. Voss, Tyler et al. (op. cit., p. 208) describe the characteristic problem solving strategy for social science problems as “Identify the cause(s) of the problem and solve by eliminating the cause(s)”. They found that a typical expert solution consisted of a few abstract solutions for a general cause, whereas novices tried to isolate different causes and defined solutions in terms of eliminating these individual causes. Another characteristic of such social science problems is that the problem solvers cannot be certain beforehand whether a chosen solution will work because it may take years before a solution is implemented and produces any observable results. Voss, Tyler et al. (1983) use the concept ‘delayed evaluation’ to describe this characteristic which prohibits the solver of social science problems to prove that a solution will work. The problem solver can Expertise used in solving wicked problems 4 only provide arguments as to why a solution may work, or is more likely to work than other solutions and thus should be preferred over another. In other words, solving, or rather evaluating, the solution of social science problems requires argumentation and since there is no way to provide formal proof that a solution will work, one needs to rely on informal reasoning. In the literature on planning and design these problems have been called ‘wicked problems’ (Kunz & Rittel, 1970; Rittel & Webber, 1984). According to Conklin and Weil (1997) such problems (a) are composed of an interlocking set of issues and constraints, rather than a definitive statement of the problem itself; (b) have many stakeholders who have expertise in different aspects of the problem to be solved, making effective problem solving more a social process than a cognitive one; (c) have constraints on the solution which change over time; and (d) often have no definitive problem, so that there is also no definitive (or right) solution; problem solving ends when time, money, energy, et cetera runs out; and (e) solutions cannot be tested and revised: one cannot try-out a highway trajectory, to use the classic example of Rittel. A major difference between the two strands of research is that whereas traditional problem solving research, such as the expert-novice paradigms used by Voss, has used single agent settings, those concentrating on ‘wicked problems’ have emphasized the involvement of multiple agents, or stakeholders, each of whom may bring a particular viewpoint on the problem. More recent research has studied the way in which teams of problem solvers coordinate their problem solving activities by sharing data and/or problem solving operators (Boshuizen & Schijf, 1998). Alpay, Giboin and Dieng (1998), for example, studied the use of representations by multi-disciplinary teams of engineers and psychologists who analyzed traffic accidents. They found that the teams used permanent representations that corresponded Expertise used in solving wicked problems 5 to systems, procedures and models on a regular basis in standard situations. Temporary representations were built dynamically during the analysis of a specific accident. Some representations were shared. For instance, all engineers and psychologists shared a simple functional model of the driver, but only the psychologists used a richer version of this model. Finally, control representations, such as models and phase decompositions, were used to guide operations on topic representations. The ultimate aim of our research is to support multi-disciplinary teams that are trying to solve social science problems by providing them with the types of (external) representations Alpay et al. (1998) identified. As a preliminary to that, we studied how monodisciplinary teams from different backgrounds approached a social science problem, namely that of high school drop-out. The background was chosen so as to compare expertise in the content of the problem to that in the process of solving the problem. Any solution to a social science problem requires formulation of a coherent set of (a) identified causes and (b) interventions of which one can argue convincingly that they will remove these causes, whilst (c) operating within acceptable constraints. We selected three groups to bring specific expertise to this problem solving. Educators were supposed to bring content knowledge of the educational system and educational policies. The two other groups were expected to bring process knowledge to the tasks: Philosophers were expected to have expertise in the areas of systematic analysis, reasoning and argumentation and ethical issues, and Debaters were expected to bring in process expertise in systematic discussion of problems and (persuasive) arguing for specific solutions and presenting these in a convincing way to their audience.

Chad Carr

Papers

Visualizing Argumentation: Software Tools for Collaborative and Educational Sense-Making

How Expert Designers Design

Use of expertise by beginning experts solving wicked problems