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John S Gero - One of the best experts on this subject based on the ideXlab platform.
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Concept formation in scientific knowledge discovery from a Constructivist View
Scientific Data Mining and Knowledge Discovery: Principles and Foundations, 2010Co-Authors: Wei Peng, John S GeroAbstract:The central goal of scientific knowledge discovery is to learn cause-effect relationships among natural phenomena presented as variables and the consequences their interactions. Scientific knowledge is normally expressed as scientific taxonomies and qualitative and quantitative laws [1]. This type of knowledge represents intrinsic regularities of the observed phenomena that can be used to explain and predict behaviors of the phenomena. It is a generalization that is abstracted and externalized from a set of contexts and applicable to a broader scope. Scientific knowledge is a type of third-person knowledge, i.e., knowledge that independent of a specific enquirer. Artificial intelligence approaches, particularly data mining algorithms that are used to identify meaningful patterns from large data sets, are approaches that aim to facilitate the knowledge discovery process [2]. A broad spectrum of algorithms has been developed in addressing classification, associative learning, and clustering problems. However, their linkages to people who use them have not been adequately explored. Issues in relation to supporting the interpretation of the patterns, the application of prior knowledge to the data mining process and addressing user interactions remain challenges for building knowledge discovery tools [3]. As a consequence, scientists rely on their experience to formulate problems, evaluate hypotheses, reason about untraceable factors and derive new problems. This type of knowledge which they have developed during their career is called first-person knowledge. The formation of scientific knowledge (third-person knowledge) is highly influenced by the enquirer's first-person knowledge construct, which is a result of his or her interactions with the environment. There have been attempts to craft automatic knowledge discovery tools but these systems are limited in their capabilities to handle the dynamics of personal experience. There are now trends in developing approaches to assist scientists applying their expertise to model formation, simulation, and prediction in various domains [4], [5]. On the other hand, first-person knowledge becomes third-person theory only if it proves general by evidence and is acknowledged by a scientific community. Researchers start to focus on building interactive cooperation platforms [1] to accommodate different Views into the knowledge discovery process. There are some fundamental questions in relation to scientific knowledge development. What aremajor components for knowledge construction and how do people construct their knowledge? How is this personal construct assimilated and accommodated into a scientific paradigm? How can one design a computational system to facilitate these processes? This chapter does not attempt to answer all these questions but serves as a basis to foster thinking along this line. A brief literature reView about how people develop their knowledge is carried out through a Constructivist View. A hydrological modeling scenario is presented to elucidate the approach. © 2010 Springer-Verlag Berlin Heidelberg.
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Scientific Data Mining and Knowledge Discovery - Concept formation in scientific knowledge discovery from a Constructivist View
Scientific Data Mining and Knowledge Discovery, 2009Co-Authors: Wei Peng, John S GeroAbstract:The central goal of scientific knowledge discovery is to learn cause–effect relationships among natural phenomena presented as variables and the consequences their interactions. Scientific knowledge is normally expressed as scientific taxonomies and qualitative and quantitative laws [1]. This type of knowledge represents intrinsic regularities of the observed phenomena that can be used to explain and predict behaviors of the phenomena. It is a generalization that is abstracted and externalized from a set of contexts and applicable to a broader scope. Scientific knowledge is a type of third-person knowledge, i.e., knowledge that independent of a specific enquirer. Artificial intelligence approaches, particularly data mining algorithms that are used to identify meaningful patterns from large data sets, are approaches that aim to facilitate the knowledge discovery process [2]. A broad spectrum of algorithms has been developed in addressing classification, associative learning, and clustering problems. However, their linkages to people who use them have not been adequately explored. Issues in relation to supporting the interpretation of the patterns, the application of prior knowledge to the data mining process and addressing user interactions remain challenges for building knowledge discovery tools [3]. As a consequence, scientists rely on their experience to formulate problems, evaluate hypotheses, reason about untraceable factors and derive new problems. This type of knowledge which they have developed during their career is called “first-person” knowledge. The formation of scientific knowledge (third-person knowledge) is highly influenced by the enquirer’s first-person knowledge construct, which is a result of his or her interactions with the environment. There have been attempts to craft automatic knowledge discovery tools but these systems are limited in their capabilities to handle the dynamics of personal experience. There are now trends in developing approaches to assist scientists applying their expertise to model formation, simulation, and prediction in various domains [4], [5]. On the other hand, first-person knowledge becomes third-person theory only if it proves general by evidence and is acknowledged by a scientific community. Researchers start to focus on building interactive cooperation platforms [1] to accommodate different Views into the knowledge discovery process. There are some fundamental questions in relation to scientific knowledge development. What aremajor components for knowledge construction and how do people construct their knowledge? How is this personal construct assimilated and accommodated into a scientific paradigm? How can one design a computational system to facilitate these processes? This chapter does not attempt to answer all these questions but serves as a basis to foster thinking along this line. A brief literature reView about how people develop their knowledge is carried out through a Constructivist View. A hydrological modeling scenario is presented to elucidate the approach.
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concept formation in scientific knowledge discovery from a Constructivist View
Scientific Data Mining and Knowledge Discovery, 2009Co-Authors: Wei Peng, John S GeroAbstract:The central goal of scientific knowledge discovery is to learn cause–effect relationships among natural phenomena presented as variables and the consequences their interactions. Scientific knowledge is normally expressed as scientific taxonomies and qualitative and quantitative laws [1]. This type of knowledge represents intrinsic regularities of the observed phenomena that can be used to explain and predict behaviors of the phenomena. It is a generalization that is abstracted and externalized from a set of contexts and applicable to a broader scope. Scientific knowledge is a type of third-person knowledge, i.e., knowledge that independent of a specific enquirer. Artificial intelligence approaches, particularly data mining algorithms that are used to identify meaningful patterns from large data sets, are approaches that aim to facilitate the knowledge discovery process [2]. A broad spectrum of algorithms has been developed in addressing classification, associative learning, and clustering problems. However, their linkages to people who use them have not been adequately explored. Issues in relation to supporting the interpretation of the patterns, the application of prior knowledge to the data mining process and addressing user interactions remain challenges for building knowledge discovery tools [3]. As a consequence, scientists rely on their experience to formulate problems, evaluate hypotheses, reason about untraceable factors and derive new problems. This type of knowledge which they have developed during their career is called “first-person” knowledge. The formation of scientific knowledge (third-person knowledge) is highly influenced by the enquirer’s first-person knowledge construct, which is a result of his or her interactions with the environment. There have been attempts to craft automatic knowledge discovery tools but these systems are limited in their capabilities to handle the dynamics of personal experience. There are now trends in developing approaches to assist scientists applying their expertise to model formation, simulation, and prediction in various domains [4], [5]. On the other hand, first-person knowledge becomes third-person theory only if it proves general by evidence and is acknowledged by a scientific community. Researchers start to focus on building interactive cooperation platforms [1] to accommodate different Views into the knowledge discovery process. There are some fundamental questions in relation to scientific knowledge development. What aremajor components for knowledge construction and how do people construct their knowledge? How is this personal construct assimilated and accommodated into a scientific paradigm? How can one design a computational system to facilitate these processes? This chapter does not attempt to answer all these questions but serves as a basis to foster thinking along this line. A brief literature reView about how people develop their knowledge is carried out through a Constructivist View. A hydrological modeling scenario is presented to elucidate the approach.
Wei Peng - One of the best experts on this subject based on the ideXlab platform.
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Concept formation in scientific knowledge discovery from a Constructivist View
Scientific Data Mining and Knowledge Discovery: Principles and Foundations, 2010Co-Authors: Wei Peng, John S GeroAbstract:The central goal of scientific knowledge discovery is to learn cause-effect relationships among natural phenomena presented as variables and the consequences their interactions. Scientific knowledge is normally expressed as scientific taxonomies and qualitative and quantitative laws [1]. This type of knowledge represents intrinsic regularities of the observed phenomena that can be used to explain and predict behaviors of the phenomena. It is a generalization that is abstracted and externalized from a set of contexts and applicable to a broader scope. Scientific knowledge is a type of third-person knowledge, i.e., knowledge that independent of a specific enquirer. Artificial intelligence approaches, particularly data mining algorithms that are used to identify meaningful patterns from large data sets, are approaches that aim to facilitate the knowledge discovery process [2]. A broad spectrum of algorithms has been developed in addressing classification, associative learning, and clustering problems. However, their linkages to people who use them have not been adequately explored. Issues in relation to supporting the interpretation of the patterns, the application of prior knowledge to the data mining process and addressing user interactions remain challenges for building knowledge discovery tools [3]. As a consequence, scientists rely on their experience to formulate problems, evaluate hypotheses, reason about untraceable factors and derive new problems. This type of knowledge which they have developed during their career is called first-person knowledge. The formation of scientific knowledge (third-person knowledge) is highly influenced by the enquirer's first-person knowledge construct, which is a result of his or her interactions with the environment. There have been attempts to craft automatic knowledge discovery tools but these systems are limited in their capabilities to handle the dynamics of personal experience. There are now trends in developing approaches to assist scientists applying their expertise to model formation, simulation, and prediction in various domains [4], [5]. On the other hand, first-person knowledge becomes third-person theory only if it proves general by evidence and is acknowledged by a scientific community. Researchers start to focus on building interactive cooperation platforms [1] to accommodate different Views into the knowledge discovery process. There are some fundamental questions in relation to scientific knowledge development. What aremajor components for knowledge construction and how do people construct their knowledge? How is this personal construct assimilated and accommodated into a scientific paradigm? How can one design a computational system to facilitate these processes? This chapter does not attempt to answer all these questions but serves as a basis to foster thinking along this line. A brief literature reView about how people develop their knowledge is carried out through a Constructivist View. A hydrological modeling scenario is presented to elucidate the approach. © 2010 Springer-Verlag Berlin Heidelberg.
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Scientific Data Mining and Knowledge Discovery - Concept formation in scientific knowledge discovery from a Constructivist View
Scientific Data Mining and Knowledge Discovery, 2009Co-Authors: Wei Peng, John S GeroAbstract:The central goal of scientific knowledge discovery is to learn cause–effect relationships among natural phenomena presented as variables and the consequences their interactions. Scientific knowledge is normally expressed as scientific taxonomies and qualitative and quantitative laws [1]. This type of knowledge represents intrinsic regularities of the observed phenomena that can be used to explain and predict behaviors of the phenomena. It is a generalization that is abstracted and externalized from a set of contexts and applicable to a broader scope. Scientific knowledge is a type of third-person knowledge, i.e., knowledge that independent of a specific enquirer. Artificial intelligence approaches, particularly data mining algorithms that are used to identify meaningful patterns from large data sets, are approaches that aim to facilitate the knowledge discovery process [2]. A broad spectrum of algorithms has been developed in addressing classification, associative learning, and clustering problems. However, their linkages to people who use them have not been adequately explored. Issues in relation to supporting the interpretation of the patterns, the application of prior knowledge to the data mining process and addressing user interactions remain challenges for building knowledge discovery tools [3]. As a consequence, scientists rely on their experience to formulate problems, evaluate hypotheses, reason about untraceable factors and derive new problems. This type of knowledge which they have developed during their career is called “first-person” knowledge. The formation of scientific knowledge (third-person knowledge) is highly influenced by the enquirer’s first-person knowledge construct, which is a result of his or her interactions with the environment. There have been attempts to craft automatic knowledge discovery tools but these systems are limited in their capabilities to handle the dynamics of personal experience. There are now trends in developing approaches to assist scientists applying their expertise to model formation, simulation, and prediction in various domains [4], [5]. On the other hand, first-person knowledge becomes third-person theory only if it proves general by evidence and is acknowledged by a scientific community. Researchers start to focus on building interactive cooperation platforms [1] to accommodate different Views into the knowledge discovery process. There are some fundamental questions in relation to scientific knowledge development. What aremajor components for knowledge construction and how do people construct their knowledge? How is this personal construct assimilated and accommodated into a scientific paradigm? How can one design a computational system to facilitate these processes? This chapter does not attempt to answer all these questions but serves as a basis to foster thinking along this line. A brief literature reView about how people develop their knowledge is carried out through a Constructivist View. A hydrological modeling scenario is presented to elucidate the approach.
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concept formation in scientific knowledge discovery from a Constructivist View
Scientific Data Mining and Knowledge Discovery, 2009Co-Authors: Wei Peng, John S GeroAbstract:The central goal of scientific knowledge discovery is to learn cause–effect relationships among natural phenomena presented as variables and the consequences their interactions. Scientific knowledge is normally expressed as scientific taxonomies and qualitative and quantitative laws [1]. This type of knowledge represents intrinsic regularities of the observed phenomena that can be used to explain and predict behaviors of the phenomena. It is a generalization that is abstracted and externalized from a set of contexts and applicable to a broader scope. Scientific knowledge is a type of third-person knowledge, i.e., knowledge that independent of a specific enquirer. Artificial intelligence approaches, particularly data mining algorithms that are used to identify meaningful patterns from large data sets, are approaches that aim to facilitate the knowledge discovery process [2]. A broad spectrum of algorithms has been developed in addressing classification, associative learning, and clustering problems. However, their linkages to people who use them have not been adequately explored. Issues in relation to supporting the interpretation of the patterns, the application of prior knowledge to the data mining process and addressing user interactions remain challenges for building knowledge discovery tools [3]. As a consequence, scientists rely on their experience to formulate problems, evaluate hypotheses, reason about untraceable factors and derive new problems. This type of knowledge which they have developed during their career is called “first-person” knowledge. The formation of scientific knowledge (third-person knowledge) is highly influenced by the enquirer’s first-person knowledge construct, which is a result of his or her interactions with the environment. There have been attempts to craft automatic knowledge discovery tools but these systems are limited in their capabilities to handle the dynamics of personal experience. There are now trends in developing approaches to assist scientists applying their expertise to model formation, simulation, and prediction in various domains [4], [5]. On the other hand, first-person knowledge becomes third-person theory only if it proves general by evidence and is acknowledged by a scientific community. Researchers start to focus on building interactive cooperation platforms [1] to accommodate different Views into the knowledge discovery process. There are some fundamental questions in relation to scientific knowledge development. What aremajor components for knowledge construction and how do people construct their knowledge? How is this personal construct assimilated and accommodated into a scientific paradigm? How can one design a computational system to facilitate these processes? This chapter does not attempt to answer all these questions but serves as a basis to foster thinking along this line. A brief literature reView about how people develop their knowledge is carried out through a Constructivist View. A hydrological modeling scenario is presented to elucidate the approach.
Joel J Mintzes - One of the best experts on this subject based on the ideXlab platform.
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Chapter 2 – Reform and Innovation in Science Teaching: A Human Constructivist View
Teaching Science for Understanding, 2020Co-Authors: Joel J MintzesAbstract:Publisher Summary This chapter describes the origins of a human Constructivist model of science teaching that can serve as an alternative to the hunches, guesses, and folklore that have guided the teaching profession for over 100 years. The final report of the Committee of Ten is one of the most remarkable documents in the history of American education. It represents for the first time that university-based scientists contributed substantially to the emerging debate on what schools teach and how they teach it. Recognizing that conceptual change often involves the extremely time-consuming process of negotiation has significant implications for curriculum and instruction. For one thing, it means that fewer topics can be covered in the course of a typical school year, and that great care needs to be taken in selecting and sequencing the concepts in a science curriculum. Increasingly, talented science teachers are being asked to take an active part in the selection of curricula, textbooks, and instructional materials. Rather than passive recipients of district-mandated curriculum guides and teacher-proof kits, these teachers are playing a central role in important decisions about curriculum and instruction.
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Chapter 3 – Research in Science Teaching and Learning: A Human Constructivist View
Teaching Science for Understanding, 2020Co-Authors: Joel J MintzesAbstract:Publisher Summary This chapter focuses on the nature of research efforts in science education and how research can contribute to the improvement of classroom practice. The 20-year period following the launch of Sputnik saw remarkable change in research activities of science educators. The establishment of the first journal that was entirely devoted to research reports in the field and the rapid expansion of graduate programs producing M.S. and Ph.D. recipients with the research skills necessary to tackle significant problems in science teaching and learning were among the most important events in 1963. After reViewing the research reports of this era, one is struck by the extent to which empirical work was driven by the demands of curriculum reform and instructional innovation. These in turn reflected the national commitment to “catch up”with Soviet advances in the military, technological, and scientific arenas. In contrast to the assumptions of many science teachers, it is now clear that learners develop a set of well-defined ideas about natural objects and events even before they arrive at the classroom door.
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assessing science understanding a human Constructivist View
2005Co-Authors: Joel J Mintzes, James H Wandersee, Joseph D NovakAbstract:J. Mintzes and J.H. Wandersee, Learning, Teaching, and Assessment: A Human Constructivist Perspective. K.M. Edmondson, Assessing Science Understanding Through Concept Maps. J.J. Mintzes and J.D. Novak, Assessing Science Understanding: The Epistemological V Diagram. S.A. Southerland, M.U. Smith, and C.L. Cummins, "What Do You Mean by That?": Using Structured InterViews to Assess Science Understanding. K. Hogan and J. Fisherkeller, Dialogue as Data: Assessing Students' Scientific Reasoning with Interactive Protocols. J.H. Wandersee, Designing an Image-Based Biology Test. E. Trowbridge and J.H. Wandersee, Observation Rubrics in Science Assessment. M.R. Vitale and N.R. Romance, Portfolios in Science Assessment: A Knowledge-Based Model for Classroom Practice. K.M. Fisher, SemNetR Software as an Assessment Tool. A.B. Champagne and V.L. Kouba, Writing to Inquire: Written Products as Performance Measures. P.M. Sadler, The Relevance of Multiple-Choice Testing in Assessing Science Understanding. P. Tamir, National and International Assessment. R.J. Shavelson and M.A. Ruiz-Primo, On the Psychometrics of Assessing Science Understanding. R.G. Good, Cautionary Notes on Assessment of Understanding Science Concepts and Nature of Science. J.J. Mintzes, J.H. Wandersee, and J.D. Novak, Epilogue: On Ways of Assessing Science Understanding. Subject Index.
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reform and innovation in science teaching a human Constructivist View
2005Co-Authors: Joel J Mintzes, James H WanderseeAbstract:Publisher Summary This chapter describes the origins of a human Constructivist model of science teaching that can serve as an alternative to the hunches, guesses, and folklore that have guided the teaching profession for over 100 years. The final report of the Committee of Ten is one of the most remarkable documents in the history of American education. It represents for the first time that university-based scientists contributed substantially to the emerging debate on what schools teach and how they teach it. Recognizing that conceptual change often involves the extremely time-consuming process of negotiation has significant implications for curriculum and instruction. For one thing, it means that fewer topics can be covered in the course of a typical school year, and that great care needs to be taken in selecting and sequencing the concepts in a science curriculum. Increasingly, talented science teachers are being asked to take an active part in the selection of curricula, textbooks, and instructional materials. Rather than passive recipients of district-mandated curriculum guides and teacher-proof kits, these teachers are playing a central role in important decisions about curriculum and instruction.
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research in science teaching and learning a human Constructivist View
2005Co-Authors: Joel J Mintzes, James H WanderseeAbstract:Publisher Summary This chapter focuses on the nature of research efforts in science education and how research can contribute to the improvement of classroom practice. The 20-year period following the launch of Sputnik saw remarkable change in research activities of science educators. The establishment of the first journal that was entirely devoted to research reports in the field and the rapid expansion of graduate programs producing M.S. and Ph.D. recipients with the research skills necessary to tackle significant problems in science teaching and learning were among the most important events in 1963. After reViewing the research reports of this era, one is struck by the extent to which empirical work was driven by the demands of curriculum reform and instructional innovation. These in turn reflected the national commitment to “catch up”with Soviet advances in the military, technological, and scientific arenas. In contrast to the assumptions of many science teachers, it is now clear that learners develop a set of well-defined ideas about natural objects and events even before they arrive at the classroom door.
Mariejose Avenier - One of the best experts on this subject based on the ideXlab platform.
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shaping a Constructivist View of organizational design science
Organization Studies, 2010Co-Authors: Mariejose AvenierAbstract:The so-called rigor—relevance gap appears unbridgeable in the classical View of organization science, which is based on the physical sciences' model. Constructivist scholars have also pointed out a certain inadequacy of this model of science for organization research, but they have not offered an explicit, alternative model of science. Responding to this lack, this paper brings together the two separate paradigmatic perspectives of Constructivist epistemologies and of organizational design science, and shows how they could jointly constitute the ingredients of a constructivism-founded scientific paradigm for organization research. Further, the paper highlights that, in this Constructivist View of organizational design science, knowledge can be generated and used in ways that are mutually enriching for academia and practice.
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shaping a Constructivist View of organizational design science
Post-Print, 2010Co-Authors: Mariejose AvenierAbstract:The so-called rigor–relevance gap appears unbridgeable in the classical View o organization science, which is based on the physical sciences' model. Constructivist scholar have also pointed out a certain inadequacy of this model of science for organization research but they have not offered an explicit, alternative model of science. Responding to this lack, this paper brings together the two separate paradigmati perspectives of Constructivist epistemologies and of organizational design science, and show how they could jointly constitute the ingredients of a constructivism-founded scientifi paradigm for organization research. Further, the paper highlights that, in this constructivis View of organizational design science, knowledge can be generated and used in ways that ar mutually enriching for academia and practice.
Hilary Asoko - One of the best experts on this subject based on the ideXlab platform.
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a case study of a teacher s progress toward using a Constructivist View of learning to inform teaching in elementary science
Science Education, 1996Co-Authors: Ken Appleton, Hilary AsokoAbstract:For some years, there have been in-service efforts to help teachers become familiar with Constructivist ideas about learning, and to apply them in their science teaching. This study is a vignette of one teacher's science teaching some time after such an in-service activity. It explores the ways in which the teacher implemented his perceptions of Constructivist ideas about learning in his teaching of a topic. The extent to which the teacher used teaching principles based on constructivism was influenced by his Views of science and of learning, how he usually planned his teaching, and his confidence in his own understanding of the topic. Features of the teaching which reflect a Constructivist View of learning are discussed and some problems are identified. We conclude with some reflections about in-service programs within a Constructivist framework. © 1996 John Wiley & Sons, Inc.