Scientific discovery learning is a highly self-directed and constructivistic form of learning. A computer simulation is a type of computer-based environment that is very suited for discovery learning, the main task of the learner being to infer, through experimentation, characteristics of the model underlying the simulation. In this article we give a review of the observed effectiveness and efficiency of discovery learning in simulation environments together with problems that learners may encounter in discovery learning, and we discuss how simulations may be combined with instructional support in order to overcome these problems. In the field of learning and instruction we now see an impressive influence of the so-called “constructivistic ” approach. In this approach a strong emphasis is placed on the learner as an active agent in the knowledge acquisition process. As in the objectivistic tradition, where developments were followed and encouraged by the computer based learning environments, such as programmed instruction, tutorials, and drill and practice programs (Alessi & Trollip, 1985), also within the constructivistic approach we find computer learning environments that help to advance developments. Examples are hypertext environments (see e.g., Gall &

High-level perception—the process of making sense of complex data at an abstract, conceptual level—is fundamental to human cognition. Through high-level perception, chaotic environmen-tal stimuli are organized into the mental representations that are used throughout cognitive pro-cessing. Much work in traditional artificial intelligence has ignored the process of high-level perception, by starting with hand-coded representations. In this paper, we argue that this dis-missal of perceptual processes leads to distorted models of human cognition. We examine some existing artificial-intelligence models—notably BACON, a model of scientific discovery, and the Structure-Mapping Engine, a model of analogical thought—and argue that these are flawed pre-cisely because they downplay the role of high-level perception. Further, we argue that perceptu-al processes cannot be separated from other cognitive processes even in principle, and therefore that traditional artificial-intelligence models cannot be defended by supposing the existence of a “representation module ” that supplies representations ready-made. Finally, we describe a model of high-level perception and analogical thought in which perceptual processing is integrated with analogical mapping, leading to the flexible build-up of representations appropriate to a given context. 1 The Problem of Perception One of the deepest problems in cognitive science is that of understanding how people make sense of the vast amount of raw data constantly bombarding them from their environment. The essence of human perception lies in the ability of the mind to hew order from this chaos, whether this means simply detecting movement in the visual field, recognizing sadness in a tone of voice, perceiving a threat on a chessboard, or coming to understand the Iran–Contra affair in terms of

The Table Lens is a visualization for searching for patterns and outliers in multivariate datasets. It supports a lightweight form of exploratory data analysis (EDA) by integrating a familiar organization, the table, with graphical representations and a small set of direct manipulation operators. We examine the EDA process as a special case of a generic process, which we call sensemaking. Using a GOMS methodology, we characterize a few central EDA tasks and compare performance of the Table Lens and one of the best of the more traditional graphical tools for EDA i.e. Splus. This analysis reveals that Table Lens is more or less on par with the power of Splus, while requiring the use of fewer specialized graphical representations. It essentially combines the graphical power of Splus with the direct manipulation and generic properties of spreadsheets and relational database front ends. We also propose a number of design refinements that are suggested by our task characterizations and analyses. Keywords Information visualization, multivariate visualization, database visualization, evaluation, GOMS, exploratory data analysis

Disciplines that study science are relatively well established in philosophy, history, and sociology. Psychology of science, by comparison, is a late bloomer but has recently shown signs of codification. The authors further this codification by integrating and reviewing the growing literature in the developmental, cognitive, personality, and social psychology of science. Only by integrating the findings from each of these perspectives can the basic questions in the study of scientific behavior be answered: Who becomes a scientist and what role do biology, family, school, and gender play? Are productivity, scientific reasoning, and theory acceptance influenced by age? What thought processes and heuristics lead to successful discovery? What personality characteristics distinguish scientists from nonscientists and eminent from less eminent scientists? Finally, how do intergroup relations and social forces influence scientific behavior? A model that integrates the consensual empirical findings from the psychology of science is pro-posed. Without the addition of a psychological dimension, I believe, it is impossible to appreciate fully the essence

This text adopts a cognitive viewpoint on design, focusing on individually conducted activities actually implemented in professional, industrial design projects. It presents elements for a cognitive descriptive model of design that, on the one hand, furthers our understanding of design, and on the other hand, offers elements to people who wish to use such knowledge in order to advance education and practice of professional designers. The text is especially concerned with dynamic aspects of design —that is, it focuses on the activity implemented by designers, especially the cognitive processes and/or strategies they use — rather than with static aspects. Section 1 presents the classical cognitive viewpoint on design, that is, the symbolic information-processing (SIP) approach, represented by Herbert A. Simon. Section 2 focuses on the main alternative to the SIP approach for design, i.e. the "situativity " (SIT) approach, mainly represented by Donald Schön. Section 3 is the main division of this text. It presents nuances and critiques with respect to both SIP and SIT approaches, and completes and integrates these two approaches into our own cognitively oriented dynamic approach to design.

investigation of search strategies for hypothesis generation using eye movement data

Artificial intelligence methods may be used to model human intelligence or to build intelligent (expert) computer systems. Al has already reached the stage of human simulation where it can model such "ineffable " phenomena as intuition, insight and inspiration. This paper reviews the empirical evidence for these capabilities. 1

In this paper, we present Simon’s approach to design, as we have described it in The Cognitive Artifacts of Designing (2006): Simon considers the sciences of design as sciences in their own right. He sees them as distinct from natural science, which is traditionally considered as “the ” “science”. “Artificial ” indeed refers to human-made as opposed to natural. For Simon, our modern world is much more an artificial, that is, a human-made, than a natural world. Together with various colleagues, Newell and Simon also used the approach to explore broader domains than the one analyzed in their famous Human Problem Solving (1972). They used it for their research into concept formation, verbal learning, and perception, but also administrative and organizational behavior, creativity and scientific discovery, and even music and emotion. It was Simon who applied to design the paradigm developed with Newell. In his analyses, he identified and elaborated various characteristics of this specific problem solving activity that have formed the basis of the approach adopted toward design by many researchers in cognitive psychology and ergonomics conducting research on design since the early 1980s. Simon: Design as a Problem-Solving Activity 1 This first chapter presents Simon's approach to design.