40,043 research outputs found
Metacognition and Reflection by Interdisciplinary Experts: Insights from Cognitive Science and Philosophy
Interdisciplinary understanding requires integration of insights from
different perspectives, yet it appears questionable whether disciplinary experts
are well prepared for this. Indeed, psychological and cognitive scientific studies
suggest that expertise can be disadvantageous because experts are often more biased
than non-experts, for example, or fixed on certain approaches, and less flexible in
novel situations or situations outside their domain of expertise. An explanation is
that experts’ conscious and unconscious cognition and behavior depend upon their
learning and acquisition of a set of mental representations or knowledge structures.
Compared to beginners in a field, experts have assembled a much larger set of
representations that are also more complex, facilitating fast and adequate perception
in responding to relevant situations. This article argues how metacognition should be
employed in order to mitigate such disadvantages of expertise: By metacognitively
monitoring and regulating their own cognitive processes and representations,
experts can prepare themselves for interdisciplinary understanding. Interdisciplinary
collaboration is further facilitated by team metacognition about the team, tasks,
process, goals, and representations developed in the team. Drawing attention to
the need for metacognition, the article explains how philosophical reflection on the
assumptions involved in different disciplinary perspectives must also be considered
in a process complementary to metacognition and not completely overlapping with
it. (Disciplinary assumptions are here understood as determining and constraining
how the complex mental representations of experts are chunked and structured.) The
article concludes with a brief reflection on how the process of Reflective Equilibrium
should be added to the processes of metacognition and philosophical reflection in
order for experts involved in interdisciplinary collaboration to reach a justifiable
and coherent form of interdisciplinary integration. An Appendix of “Prompts or
Questions for Metacognition” that can elicit metacognitive knowledge, monitoring,
or regulation in individuals or teams is included at the end of the article
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The effect of multiple knowledge sources on learning and teaching
Current paradigms for machine-based learning and teaching tend to perform their task in isolation from a rich context of existing knowledge. In contrast, the research project presented here takes the view that bringing multiple sources of knowledge to bear is of central importance to learning in complex domains. As a consequence teaching must both take advantage of and beware of interactions between new and existing knowledge. The central process which connects learning to its context is reasoning by analogy, a primary concern of this research. In teaching, the connection is provided by the explicit use of a learning model to reason about the choice of teaching actions. In this learning paradigm, new concepts are incrementally refined and integrated into a body of expertise, rather than being evaluated against a static notion of correctness. The domain chosen for this experimentation is that of learning to solve "algebra story problems." A model of acquiring problem solving skills in this domain is described, including: representational structures for background knowledge, a problem solving architecture, learning mechanisms, and the role of analogies in applying existing problem solving abilities to novel problems. Examples of learning are given for representative instances of algebra story problems. After relating our views to the psychological literature, we outline the design of a teaching system. Finally, we insist on the interdependence of learning and teaching and on the synergistic effects of conducting both research efforts in parallel
THE "POWER" OF TEXT PRODUCTION ACTIVITY IN COLLABORATIVE MODELING : NINE RECOMMENDATIONS TO MAKE A COMPUTER SUPPORTED SITUATION WORK
Language is not a direct translation of a speaker’s or writer’s knowledge or intentions. Various complex processes and strategies are involved in serving the needs of the audience: planning the message, describing some features of a model and not others, organizing an argument, adapting to the knowledge of the reader, meeting linguistic constraints, etc. As a consequence, when communicating about a model, or about knowledge, there is a complex interaction between knowledge and language. In this contribution, we address the question of the role of language in modeling, in the specific case of collaboration over a distance, via electronic exchange of written textual information. What are the problems/dimensions a language user has to deal with when communicating a (mental) model? What is the relationship between the nature of the knowledge to be communicated and linguistic production? What is the relationship between representations and produced text? In what sense can interactive learning systems serve as mediators or as obstacles to these processes
Collaboration scripts - a conceptual analysis
This article presents a conceptual analysis of collaboration scripts used in face-to-face and computer-mediated collaborative learning. Collaboration scripts are scaffolds that aim to improve collaboration through structuring the interactive processes between two or more learning partners. Collaboration scripts consist of at least five components: (a) learning objectives, (b) type of activities, (c) sequencing, (d) role distribution, and (e) type of representation. These components serve as a basis for comparing prototypical collaboration script approaches for face-to-face vs. computer-mediated learning. As our analysis reveals, collaboration scripts for face-to-face learning often focus on supporting collaborators in engaging in activities that are specifically related to individual knowledge acquisition. Scripts for computer-mediated collaboration are typically concerned with facilitating communicative-coordinative processes that occur among group members. The two lines of research can be consolidated to facilitate the design of collaboration scripts, which both support participation and coordination, as well as induce learning activities closely related to individual knowledge acquisition and metacognition. In addition, research on collaboration scripts needs to consider the learners’ internal collaboration scripts as a further determinant of collaboration behavior. The article closes with the presentation of a conceptual framework incorporating both external and internal collaboration scripts
Eliciting Expertise
Since the last edition of this book there have been rapid developments in the use and exploitation of formally elicited knowledge. Previously, (Shadbolt and Burton, 1995) the emphasis was on eliciting knowledge for the purpose of building expert or knowledge-based systems. These systems are computer programs intended to solve real-world problems, achieving the same level of accuracy as human experts. Knowledge engineering is the discipline that has evolved to support the whole process of specifying, developing and deploying knowledge-based systems (Schreiber et al., 2000) This chapter will discuss the problem of knowledge elicitation for knowledge intensive systems in general
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