I examined the proposition that there are psychological limits on what scientific problems can be solved, and that these limits may be based on a failure to be able to produce imagable, observation-based models for any possible solution, a position suggested by philosopher Colin McGinn in an argument attempting to prove that the mind-body problem is unsolvable. I examined another likely candidate for an unsolvable problem -- the ultimate origin of the universe (i.e., what might have preceded the Big Bang (...) or any other starting point; why there should be something rather than nothing) -- by exploring the reasoning of physicists about this problem and measuring visual imagery frequency and vividness, with the expectation that those who most believed the problem unsolvable would be more frequent/vivid imagers and therefore more affected by the apparent impossibility of producing an imagable solution. Eight physicists were interviewed and imagery frequency and vividness measurements performed using Cohen & Saslona's IDQ-IHS and Marks's VVIQ, respectively. All subjects considered the problem unsolvable within today's physics and all but one thought the problem still meaningful, though none were optimistic about a solution. The one subject who dismissed the problem had the lowest imagery frequency score, and there was also a significant rank order correlation (r = 0.83, p < .02) between degree of belief in problem unsolvability (extended to include viewing the problem as meaningful and not already solved) and a composite imagery frequency/vividness score, though the sample was too small to control for some possible confounding factors. (shrink)

In the course of our educational life we are introduced to various subject areas, each with its specific way of representing knowledge. The challenge for the learner is to be able to think in ways that are supported by, and match, the representational format. A fundamental question for the science of learning concerns how this is achieved. In this thesis, it will be argued that by observing individuals collaboratively constructing their own graphic representations in a subject area that is new (...) to them, it is possible to gain insight into this process. In the LUMES Master program, students are introduced to systems thinking and the method of creating Causal Loop Diagrams and interactive computer models for analyzing and solving complex problems. They learn how to use the terminology, symbols and methods of a specific semiotic domain. The research questions guiding the analysis of the empirical data concern how interacting with systems thinking tools affect students in the process of collaboratively analyzing and modeling complex problems, and how cognitive and semiotic resources are used in this process. The study provides a conceptual lens with which to discuss and perceive the complexity of learning to express knowledge with new representational tools: both its visual and spatial formalism and the related terminology. Three theoretical perspectives are applied: the socio-cultural perspective, the social semiotic, and the constructivist perspective. The sociocultural perspective is applied to emphasize the use of tools as a part of human learning. It puts focus on the mediating roles of language and other tools in a social context, which is in many ways connected to the social semiotic perspective. To view systems thinking and System Dynamics as a semiotic domain accentuates the use of specific terminology, symbols and gestures to express understanding. Furthermore, the constructivist perspective is used to put focus on the role of individual cognitive abilities and prior knowledge. Three empirical studies provide detailed analyses of video-recorded collaborative work in System Dynamics education. The case studies show how individual cognitive abilities together with language, gesture and the visual representations are used as integrated resources to express understanding of a problem and its solution. (shrink)

Practical wisdom (hereafter simply “wisdom”) is the intellectual virtue that enables a person to make reliably good decisions about how, all-things-considered, to live and conduct herself. Because wisdom is such an important and high-level achievement, we should wonder: what is the nature of wisdom? What kinds of skills, habits and capacities does it involve? Can real people actually develop it? If so, how? I argue that we can answer these questions by modeling wisdom on expert decision-making skill in complex areas (...) like firefighting. I develop this expert skill model of wisdom using philosophical argument informed by relevant empirical research. I begin in Chapter 1 by examining the historical roots of analogies between wisdom and practical skills in order to motivate the expert skill model. In Chapter 2, I provide the core argument for the expert skill model. I then use the remaining chapters to pull out the implications of the expert skill model. In Chapter 3, I show that the expert skill model yields practical guidance about how to develop wisdom. In Chapter 4, I address the objection, due to Daniel Jacobson, that wisdom is not a skill that humans could actually develop, since skill development requires a kind of feedback in practice that is not available for all-things-considered decisions about how to live. Finally, in Chapter 5, I apply the expert skill model to the question, much discussed by virtue ethicists, of whether a wise person deliberates using a comprehensive and systematic conception of the good life. (shrink)

Collaborative learning in computer-supported learning environments typically means that learners work on tasks together, discussing their individual perspectives via text-based media or videoconferencing, and consequently acquire knowledge. Collaborative learning, however, is often sub-optimal with respect to how learners work on the concepts that are supposed to be learned and how learners interact with each other. Therefore, instructional support needs to be implemented into computer-supported collaborative learning environments. One possibility to improve collaborative learning environments is to conceptualize scripts that structure epistemic (...) activities and social interactions of learners. In this contribution, two studies will be reported that investigated the effects of epistemic and social scripts in a text-based computer-supported learning environment and in a videoconferencing learning environment in order to foster the individual acquisition of knowledge. In each study the factors "epistemic script" and "social script" have been independently varied in a 2×2-factorial design. 182 university students of Educational Science participated in these two studies. Results of both studies show that social scripts can be substantially beneficial with respect to the individual acquisition of knowledge, whereas epistemic scripts apparently do not lead to the expected effects. (shrink)

Learning to construct good scientific explanations is an important aspect of learning science. To this end it is important to also consider that the detailed standards for good explanations differ across the sciences. Practitioners face these differences, for instance, when interdisciplinary work is attempted. This paper reports on a comparative qualitative study of exemplar explanations on polymers from molecular biology and polymer physics aiming to map the differences in standards for good explanations between the two domains. The study gives detail (...) to the theoretical expectation that mechanistic explanations are the ideal in molecular biology whereas derivations play the central role in polymer physics. Mechanistic explanations in molecular biology focus on material objects with a function-determining structure. In polymer physics derivations are the preferred kind of explanation. Derivations focus on variables, particularly, physical quantities like energy and entropy, whereas the three dimensional structure of polymers is often ignored. Differences in the kinds of explanations-seeking questions deemed relevant within the two domains are also identified. (shrink)