this article is to develop a task structure for design as a knowledge -based problemsolving activity

In this paper, we report about the design, development, and implementation of the SEPIA cooperative hypermedia authoring environment. It provides results on the following aspects of SEPIA: persistent and shared data storage, hypermedia data model with composites, sophisticated and comprehensive authoring functionality, support for a new rhetoric and for cooperative work. We start by identifying the challenge of hypermedia authoring and production which serves as the driving force for our development. Using interacting problem spaces as the vehicle for modelling the dynamic aspects of authoring, we arrive at a set of requirements answered by the concept of "activity spaces". The design of coherent hyperdocuments is facilitated by our "construction kit". Furthermore, we describe the extensions and modifications necessary to support multiple authors with the cooperative version of SEPIA. The central issue of the paper is the system architecture and its implementation. We describe the basi...

this paper. In particular, the protocols we have collected often involve picturing different situations (e.g., a mental map of South America, images of savannas, or an advertisement showing Juan Valdez on his coffee plantation in Colombia). These im- ages can be taken as evidence for the manipulation of mental models in Johnson-Laird's terms. But overlaying this manipulation of mental models are the systematic patterns in which they are deployed to support one's con- clusions (cf. Rips, 1986). So while mental models may be part of the story of plausible reasoning, there is another critical part which the theory we pro- pose addresses. The theory does not address the issue of whether people make systematic errors in their reasoning, as the psychological literature on decision making (Kahneman, Slovic, & Tversky, 1982) attempts to document. This issue does not arise in the theory because we are developing a formalism for representing the kinds of inferences people make and the parameters that affect their certainty, rather than a theory about how people make particular inferences. People may systematically ignore some kinds of information or undervalue particular certainty parameters--we have not attempted to determine whether they do or not. Instead we have tried to represent all the kinds of reasoning patterns and the kinds of certainty parameters that appear in the protocols we have analyzed (Collins, 1978a, 1978b). In this regard it is worth pointing out that certain fallacles in logic, such as affirming the consequent (Havi- land, 1974), become plausible inference patterns in the theory.' The theory was developed to account for protocols where. a question drives the search fo relevant information; in Artificial Intelligence this is called backward inferencing. One qu...

. The aim of this paper is to understand what is involved in parametric design problem solving. In order to achieve this goal, in this paper i) we identify and detail the conceptual elements defining a parametric design task specification; ii) we illustrate how these elements are interpreted and operationalised during the design process; and iii) we formulate a generic model of parametric design problem solving. We then re-describe a number of problem solving methods in terms of the proposed generic model and we show that such a re-description enables us to provide a more precise account of the different competence behaviours expressed by the methods in question. 1. INTRODUCTION Design is about constructing artifacts. This means that, broadly speaking, any design process is 'creative', in the sense that a design process produces a 'new solution', as opposed to selecting a solution from a predefined set. While recognizing the essential creative elements present in any design process, ...

This paper is about syllogistic reasoning, i.e., reasoning from such pairs of premises as, All the chefs are musicians; some of the musicians are painters. We present a computer model that implements the latest account of syllogisms, which is based on the theory of mental models. We also report four experiments that were designed to test this account. Experiments 1 and 2 examined the strategies revealed by the participants ’ use of paper and pencil as aids to reasoning. Experiment 3 used a new technique to externalize thinking. The participants had to refute, if possible, putative conclusions by constructing external models that were examples of the premises but counterexamples of the conclusions. Experiment 4 used the same techniques to examine the participants ’ strategies as they drew their own conclusions from syllogistic premises. The results of the experiments showed that individuals not trained in logic can construct counterexamples, that they use similar operations to those implemented in the computer model, but that they rely on a much greater variety of interpretations of premises and of search strategies than the computer model does. We re-evaluates current theories of syllogistic reasoning in the light of these results. I.

The notion of generic design, while it has been around for 25 years is not often articulated, especially within Newell and Simon's (1972) Information Processing Theory framework. Design is merely lumped in with other forms of problem solving activity. Intuitively one feels that there should be a level of description of the phenomenon which refines this broad classification by further distinguishing between design and non-design problem solving. However, Information Processing Theory does not facilitate such problem classification. This paper makes a preliminary attempt to differentiate design problem solving from non-design problem solving by identifying major invariants in the design problem space.

After identifying essential student modeling issues and machine learning approaches, this paper examines how machine learning techniques have been used to automate the construction of student models as well as the background knowledge necessary for student modeling. In the process, the paper sheds light on the difficulty, suitability and potential of using machine learning for student modeling processes, and, to a lesser extent, the potential of using student modeling techniques in machine learning.

Abstract This paper outlines an original interactivist-constructivist (I-C) approach to modeling intelligence and learning as a dynamical embodied form of adaptiveness and explores some applications of I-C to understanding the way cognitive learning is realised in the brain. Two key ideas for conceptualising intelligence within this framework are developed. These are: (i) intelligence is centrally concerned with the capacity for coherent, contextsensitive, self-directed management of interaction, (ii) the primary model for cognitive learning is anticipative skill construction. Self-directedness is a capacity for integrative process modulation which allows a system to ‘steer ’ itself through its world by anticipatively matching its own viability requirements to interaction with its environment. Because the adaptive interaction processes required of intelligent systems are too complex for effective action to be prespecified (e.g. genetically) learning is an important component of intelligence. A model of self-directed anticipative learning (SDAL) is formulated based on interactive skill construction, and argued to constitute a central constructivist process involved in cognitive development. SDAL illuminates the capacity of intelligent learners to start with the vague, poorly defined, problems typically posed in realistic learning situations and progressively refine them, transforming them into problems with sufficient structure to guide the construction of a solution. Finally, some of the implications of I-C for modeling of the neuronal basis of intelligence and learning are explored; in particular, Quartz and Sejnowski’s recent neural constructivism (NC) paradigm, enriched by Montague and Sejnowski’s dopaminergic model of anticipative-predictive neural learning, is assessed as a promising, but incomplete, contribution to this approach. The paper concludes with a four-fold reflection on the divergence in cognitive modeling philosophy between the I-C and the traditional computational information processing (CIP) approaches. 1.

We present an empirical study investigating collaborative and individual decision-making about data using two different information visualization systems. Based on previous research, one system is considered more transparent than the other in terms of visual representation and functionality. We found that people who worked in groups were more correct in their answers for objective questions, based on searching a large dataset. These results held for the more transparent system, but not the less transparent system. In a second task, groups were more accurate in their results for a free data discovery task. Again, these results held for the more transparent system only. Subjects using this system also produced results that were higher in complexity but judged lower in importance. Groups and individuals did not differ. We suggest that given the right visualization system, groups do better than individuals in finding more accurate results, but not necessarily increased or more meaningful results.

Abstract: Our attempts at describing the processes involved in learning and knowledgebuilding activities depend on our ways of conceptualizing the context in which such activities take place. Here we trace the development of the concept of “problem space ” from its inception within the information-processing perspective as a characterization of individual problem-solving activity. We review reformulations and extensions made to the concept within the Learning Sciences, and explore them as attempts to better describe small-group interactions in complex knowledge-building contexts. Using a detailed analysis of sustained, online collaborative problem-solving activity, we propose that a new aspect of the problem space needs to be carefully considered in order to fully account for these kinds of experiences: temporal and sequential orientation to inter-subjective meaning making.