Results for 'conceptual learning'

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  1.  15
    Conceptual Learning and Local Incommensurability: A Dynamic Logic Approach.Corina Strößner - 2022 - Axiomathes 32 (6):1025-1045.
    In recent decades, the logical study of rational belief dynamics has played an increasingly important role in philosophy. However, the dynamics of concepts such as conceptual learning received comparatively little attention within this debate. This is problematic insofar as the occurrence of conceptual change (especially in the sciences) has been an influential argument against a merely logical analysis of beliefs. Especially Kuhn’s ideas about the incommensurability, i.e., untranslatability, of succeeding theories seem to stand in the way of (...)
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  2.  67
    Conceptual Learning: The Priority for Higher Education.Effie Maclellan - 2005 - British Journal of Educational Studies 53 (2):129 - 147.
    The common sense notion of learning as the all-pervasive acquisition of new behaviour and knowledge, made vivid by experience, is an incomplete characterisation, because it assumes that the learning of behaviour and the learning of knowledge are indistinguishable, and that acquisition constitutes learning without reference to transfer. A psychological level of analysis is used to argue that conceptual learning should have priority in higher education.
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  3.  96
    Consciousness and conceptual learning in a socially situated agent.Myles Bogner, Uma Ramamurthy & Stan Franklin - 2000 - In Kerstin Dauthenhahn (ed.), Human Cognition and Social Agent Technology. Amsterdam: John Benjamins. pp. 113--135.
  4.  28
    Interactions between perceptual and conceptual learning.Robert Goldstone, Mark Steyvers, Jesse Spencer-Smith & Alan Kersten - 2000 - In Eric Dietrich Art Markman (ed.), Cognitive Dynamics: Conceptual Change in Humans and Machines. Lawrence Erlbaum.
  5.  21
    Some factors in the programming of conceptual learning.Robert M. Gagné & Larry T. Brown - 1961 - Journal of Experimental Psychology 62 (4):313.
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  6.  12
    The effect of verbal reinforcement combinations on conceptual learning.Arnold H. Buss & Edith H. Buss - 1956 - Journal of Experimental Psychology 52 (5):283.
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  7.  16
    Learning outcomes and the learner's consistency seeking in rote and conceptual learning.Nicholas M. Sanders & Ovid J. Tzeng - 1973 - Journal of Experimental Psychology 98 (2):302.
  8. Inhibitors and facilitators of peer interaction that supports conceptual learning: The role of achievement goal orientations.C. S. C. Asterhan, B. B. Schwarz & R. Butler - 2009 - In N. A. Taatgen & H. van Rijn (eds.), Proceedings of the 31st Annual Conference of the Cognitive Science Society.
     
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  9.  10
    The Influence of Teaching Approach on Students’ Conceptual Learning in Physics.Lucia Bigozzi, Christian Tarchi, Carlo Fiorentini, Paola Falsini & Federica Stefanelli - 2018 - Frontiers in Psychology 9.
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  10.  23
    Modeling the perceptual component of conceptual learning—a coordination perspective.William J. Clancey - 2005 - In Peter Gardenfors, Petter Johansson & N. J. Mahwah (eds.), Cognition, Education, and Communication Technology. Erlbaum Associates. pp. 109--146.
  11.  40
    Conceptual challenges for interpretable machine learning.David S. Watson - 2022 - Synthese 200 (2):1-33.
    As machine learning has gradually entered into ever more sectors of public and private life, there has been a growing demand for algorithmic explainability. How can we make the predictions of complex statistical models more intelligible to end users? A subdiscipline of computer science known as interpretable machine learning (IML) has emerged to address this urgent question. Numerous influential methods have been proposed, from local linear approximations to rule lists and counterfactuals. In this article, I highlight three (...) challenges that are largely overlooked by authors in this area. I argue that the vast majority of IML algorithms are plagued by (1) ambiguity with respect to their true target; (2) a disregard for error rates and severe testing; and (3) an emphasis on product over process. Each point is developed at length, drawing on relevant debates in epistemology and philosophy of science. Examples and counterexamples from IML are considered, demonstrating how failure to acknowledge these problems can result in counterintuitive and potentially misleading explanations. Without greater care for the conceptual foundations of IML, future work in this area is doomed to repeat the same mistakes. (shrink)
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  12.  34
    What Conceptual Engineering Can Learn from the History of Philosophy of Science: Healthy Externalism and Metasemantic Plasticity.Matteo De Benedetto - 2024 - Hopos: The Journal of the International Society for the History of Philosophy of Science 14 (1):1-24.
    Conceptual engineering wants analytic philosophy to be centered around the assessment and improvement of philosophical concepts. But contemporary debates about conceptual engineering do not engage much with the vast literature on conceptual change that exists in philosophy of science. In this article, I argue that an adequate appreciation of the history of philosophy of science can contribute to discussions about conceptual engineering. Specifically, I show that the evolution of debates over scientific conceptual change arguably demonstrates (...)
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  13. Moral Learning: Conceptual foundations and normative relevance.Peter Railton - 2017 - Cognition 167 (C):172-190.
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  14.  63
    Conceptual development and the paradox of learning.Michael Luntley - 2008 - Journal of Philosophy of Education 42 (1):1-14.
    Conceptual development requires learning. It requires learning to make discriminations that were previously unavailable to the subject. Notwithstanding the descriptions of learning available in the psychological and educational literature, there is no account available that shows that it is so much as possible. There can be no such account unless there is an answer to Jerry Fodor's paradox of learning. On our current understanding of concept acquisition, there is no such thing as learning. In (...)
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  15.  16
    Conceptual Development and the Paradox of Learning.Michael Luntley - 2008 - Journal of Philosophy of Education 42 (1):1-14.
    Conceptual development requires learning. It requires learning to make discriminations that were previously unavailable to the subject. Notwithstanding the descriptions of learning available in the psychological and educational literature, there is no account available that shows that it is so much as possible. There can be no such account unless there is an answer to Jerry Fodor’s paradox of learning. On our current understanding of concept acquisition, there is no such thing as learning. In (...)
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  16. Learning and the Necessity of Non-Conceptual Content in Sellars's Empiricism and the Philosophy of Mind.David Forman - 2006 - In Michael P. Wolf & Mark Lance (eds.), The Self-Correcting Enterprise: Essays on Wilfrid Sellars. Rodopi. pp. 115-145.
    For Sellars, the possibility of empirical knowledge presupposes the existence of "sense impressions" in the perceiver, i.e., non-conceptual states of perceptual consciousness. But this role for sense impressions does not implicate Sellars' account in the Myth of the Given: sense impressions do not stand in a justificatory relation to instances of perceptual knowledge; their existence is rather a condition for the possibility of the acquisition of empirical concepts. Sellars suggests that learning empirical concepts presupposes that we can remember (...)
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  17.  13
    Conceptualization and Measurement of Flow in a Chinese Blended English as a Foreign Language Learning Context.Xin Wang & Chenguang Huang - 2022 - Frontiers in Psychology 13.
    This study takes a holistic view of flow and anti-flow experiences as interactive subsystems in blended English as a foreign language learning and examines the dynamic complex construct in the field of instructed second language acquisition. We first rephrased the 22-item Classroom Flow Questionnaire to better reflect the context of blended EFL learning. The modified CFQ was then administered to 661 first language Chinese EFL learners. A final 14-item Foreign Language Flow Scale was developed based on results from (...)
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  18.  32
    Conceptual Hierarchies in a Flat Attractor Network: Dynamics of Learning and Computations.Christopher M. O’Connor, George S. Cree & Ken McRae - 2009 - Cognitive Science 33 (4):665-708.
    The structure of people’s conceptual knowledge of concrete nouns has traditionally been viewed as hierarchical (Collins & Quillian, 1969). For example, superordinate concepts (vegetable) are assumed to reside at a higher level than basic‐level concepts (carrot). A feature‐based attractor network with a single layer of semantic features developed representations of both basic‐level and superordinate concepts. No hierarchical structure was built into the network. In Experiment and Simulation 1, the graded structure of categories (typicality ratings) is accounted for by the (...)
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  19.  84
    Skill Learning and Conceptual Thought: Making our way through the wilderness.Ellen Fridland - 2014 - In Bana Bashour Hans Muller (ed.), Contemporary Philosophical Naturalism and Its Implications. Routledge.
  20.  32
    Word learning is 'smart': evidence that conceptual information affects preschoolers' extension of novel words.A. Booth - 2002 - Cognition 84 (1):B11-B22.
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  21.  49
    Learning natural numbers is conceptually different than learning counting numbers.Dwight Read - 2008 - Behavioral and Brain Sciences 31 (6):667-668.
    How children learn number concepts reflects the conceptual and logical distinction between counting numbers, based on a same-size concept for collections of objects, and natural numbers, constructed as an algebra defined by the Peano axioms for arithmetic. Cross-cultural research illustrates the cultural specificity of counting number systems, and hence the cultural context must be taken into account.
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  22.  16
    A Conceptual Framework Over Contextual Analysis of Concept Learning Within Human-Machine Interplays.Farshad Badie - 2017 - In Emerging Technologies for Education. Cham, Switzerland: pp. 65-74.
    This research provides a contextual description concerning an existential and structural analysis of ‘Relations’ between human beings and machines. Subsequently, it will focus on the conceptual and epistemological analysis of (i) my own semantics-based framework [for human meaning construction] and of (ii) a well-structured machine concept learning framework. Accordingly, I will, semantically and epistemologically, focus on linking those two frameworks for logical analysis of concept learning in the context of human-machine interrelationships. It will be demonstrated that the (...)
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  23.  15
    Concept learning as a function of the conceptual rule and the availability of positive and negative instances.L. E. Bourne, Bruce R. Ekstrand & Bonnie Montgomery - 1969 - Journal of Experimental Psychology 82 (3):538.
  24.  19
    Learning conceptual rules: I. Some interrule transfer effects.Lyle E. Bourne Jr & Donald E. Guy - 1968 - Journal of Experimental Psychology 76 (3p1):423.
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  25.  25
    A Conceptual Model of Learning Agility and Authentic Leadership Development: Moderating Effects of Learning Goal Orientation and Organizational Culture.Shikha Dixit & Nidhi Yadav - 2017 - Journal of Human Values 23 (1):40-51.
    The present article attempts to examine the relationship between learning agility and authentic leadership development. The recent rise in the cases of fraud involving leaders at various levels in the organizations, and the resulting distrust among the employees towards their leaders, points to the need to have authentic leaders, given the positive organizational outcomes associated with this leadership approach. Further, scholars have suggested that leadership development is possible through learning from one’s life experiences. The developmental definition of authentic (...)
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  26. Conceptualizations of argumentation from science studies and the learning sciences and their implications for the practices of science education.Leah A. Bricker & Philip Bell - 2008 - Science Education 92 (3):473-498.
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  27.  16
    Conceptual Integration and Empirical Validation of a Unified Taxonomy: Quantitative Data Analysis for Virtual Learning Environments.Melanie Moreno-Barahona, Blanca Fraijo-Sing, Ghozlane Fleury-Bahi, Oscar Navarro-Carrascal & Cesar Tapia-Fonllem - 2022 - Frontiers in Psychology 13.
    Specific classes of cyberspaces emphasize different types of digital transactions given the user’s context, thus making it essential to take into account what these environments can afford. In this way, we can portray the niches of technological use as ecologies of particular possibilities and compare how they differ between distinct spheres of human life. The present research is focused on describing the conceptual integration of a taxonomic crossover between Virtual Learning Environments and Educational Affordances of Technology, while also (...)
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  28.  16
    Learning conceptual rules: II. The role of positive and negative instances.Lyle E. Bourne & Donald E. Guy - 1968 - Journal of Experimental Psychology 77 (3p1):488.
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  29.  40
    Meaningful learning: The essential factor for conceptual change in limited or inappropriate propositional hierarchies leading to empowerment of learners.Joseph D. Novak - 2002 - Science Education 86 (4):548-571.
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  30.  2
    Conceptualizing truth: implications for teaching and learning.Kevin S. Krahenbuhl - 2022 - Charlotte, NC: Information Age Publishing.
    It has been widely noted that society has moved away from seeing truth as an objective and, in some ways, important part of what it means to be educated. Varied conceptions of truth have existed and have been debated in the halls of academia for years but recently a shift has occurred in which truth has lost its status broadly as a virtue. In fact, in 2016, Oxford Dictionary declared "post-truth" as its international word of the year, defined as: 'relating (...)
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  31. Learning goals in an exemplary science teacher's practice: Cognitive and social factors in teaching for conceptual change.Michael E. Beeth & Peter W. Hewson - 1999 - Science Education 83 (6):738-760.
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  32. A Meeting of the Conceptual and the Natural: Wittgenstein on Learning a Sensation‐Language.Hao Tang - 2014 - Philosophy and Phenomenological Research 91 (1):105-135.
    Since the rise of modern natural science there has been deep tension between the conceptual and the natural. Wittgenstein's discussion of how we learn a sensation-language contains important resources that can help us relieve this tension. The key here, I propose, is to focus our attention on animal nature, conceived as partially re-enchanted. To see how nature, so conceived, helps us relieve the tension in question, it is crucial to gain a firm and detailed appreciation of how the primitive-instinctive, (...)
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  33.  28
    Learning and conceptual content in Reid's theory of perception.Ryan Nichols - 2002 - British Journal for the History of Philosophy 10 (4):561-590.
  34.  8
    Learning Environment in Light of Positional, Institutional, and Cultural Interpretations: An Empirically-Based Conceptual Analysis.Kovač Velibor Bobo, Lund Ingrid & Omdal Heidi - 2017 - Educational Studies: A Jrnl of the American Educ. Studies Assoc 53 (1):78-94.
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  35. From conceptual change to transformative modeling: A case study of an elementary teacher in learning astronomy.Ji Shen & Jere Confrey - 2007 - Science Education 91 (6):948-966.
  36. Active‐Constructive‐Interactive: A Conceptual Framework for Differentiating Learning Activities.Michelene T. H. Chi - 2009 - Topics in Cognitive Science 1 (1):73-105.
    Active, constructive, and interactive are terms that are commonly used in the cognitive and learning sciences. They describe activities that can be undertaken by learners. However, the literature is actually not explicit about how these terms can be defined; whether they are distinct; and whether they refer to overt manifestations, learning processes, or learning outcomes. Thus, a framework is provided here that offers a way to differentiate active, constructive, and interactive in terms of observable overt activities and (...)
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  37. Learning about biological evolution: A special case of intentional conceptual change.S. A. Southerland & G. M. Sinatra - 2003 - In Gale M. Sinatra & Paul R. Pintrich (eds.), Intentional Conceptual Change. L. Erlbaum. pp. 317--345.
     
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  38. Conceptualizing Truth in Teaching and Learning: Implications of Truth Seeking for Feminist Practice.Becky Ropers-Huilman - 1998 - In Bat-Ami Bar On & Ann Ferguson (eds.), Daring to Be Good: Essays in Feminist Ethico-Politics. Routledge. pp. 55--69.
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  39. Understanding teacher and learning movement between real-world and classroom genres via conceptual integration.Fiona Jackson - 2015 - In Wayne Hugo (ed.), Conceptual integration and educational analysis. Cape Town, South Africa: HSRC Press.
     
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  40. Studying conceptual change in learning physics (vol 76, pg 615, 1992).Cf di DykstraBoyle & Ia Monarch - 1993 - Science Education 77 (2):259-259.
     
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  41. Learning and utilization of conceptual rules.Lyle E. Bourne Jr - 1967 - In Benjamin Kleinmuntz (ed.), Concepts and the Structure of Memory. Wiley.
     
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  42.  6
    Conceptual Hierarchies in a Flat Attractor Network: Dynamics of Learning and Computations.Ken McRae Christopher M. O'Connor, George S. Cree - 2009 - Cognitive Science 33 (4):665.
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  43. Learning and Conceptual Change: The View from the Neurons.Paul M. Churchland - 1996 - In Andy Clark & Peter Millican (eds.), Connectionism, Concepts, and Folk Psychology: The Legacy of Alan Turing, Volume 2. Clarendon Press.
  44. Learning and Conceptual Change: The View from the Neurons.Paul M. Churchland - 1999 - In Andy Clark & Peter Millican (eds.), Connectionism, Concepts, and Folk Psychology: The Legacy of Alan Turing, Volume Ii. Clarendon Press.
  45. Studying conceptual change in learning physics.Dewey I. Dykstra, C. Franklin Boyle & Ira A. Monarch - 1992 - Science Education 76 (6):615-652.
  46.  7
    Informed learning and conceptual structure: Putting the “Birdness” back in the Bird.Kenneth Kurtz - 1997 - Behavioral and Brain Sciences 20 (1):75-76.
    The computational notion of “trading spaces” is highly relevant to the psychological domain of categorization. The “theory” view of concepts can be interpreted as a recoding view. A design principle for exploiting learned recodings in order to handle the type-2 problem of forming sophisticated concepts is outlined.
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  47.  5
    Keeping conceptual boundaries distinct between decision making and learning is necessary to understand social influence.Gaël Le Mens - 2014 - Behavioral and Brain Sciences 37 (1):87-88.
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  48. Introduction: Conceptualizing truth; implication for teaching and learning.Kevin S. Krahenbuhl - 2022 - In Conceptualizing Truth: Implications for Teaching and Learning. Information Age Publishing.
     
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  49.  2
    Conceptual inductive learning.Miroslav Kubat - 1991 - Artificial Intelligence 52 (2):169-182.
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  50. Learning as conceptual change: Factors mediating the development of preservice elementary teachers' views of nature of science.Fouad Abd‐El‐Khalick & Valarie L. Akerson - 2004 - Science Education 88 (5):785-810.
     
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