Literatura académica sobre el tema "Cognitive learning theory"
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Artículos de revistas sobre el tema "Cognitive learning theory"
Kolluru, Mythili. "Cognitive Style, Learning Preference and Performance: Theory and Empirics". International Journal of Psychosocial Rehabilitation 24, n.º 4 (28 de febrero de 2020): 3678–88. http://dx.doi.org/10.37200/ijpr/v24i4/pr201481.
Texto completoGlenn, Cynthia Wheatley. "Cognitive Free will Learning Theory". Procedia - Social and Behavioral Sciences 97 (noviembre de 2013): 292–98. http://dx.doi.org/10.1016/j.sbspro.2013.10.236.
Texto completoBurden, Robert. "Mediated Learning Theory". School Psychology International 8, n.º 1 (enero de 1987): 59–62. http://dx.doi.org/10.1177/014303438700800108.
Texto completoHapps, John C. "Cognitive Learning Theory and Classroom Complexity". Research in Science & Technological Education 3, n.º 2 (enero de 1985): 159–74. http://dx.doi.org/10.1080/0263514850030109a.
Texto completoMcSparron, Jakob I., Anita Vanka y C. Christopher Smith. "Cognitive learning theory for clinical teaching". Clinical Teacher 16, n.º 2 (23 de marzo de 2018): 96–100. http://dx.doi.org/10.1111/tct.12781.
Texto completoGoldfarb, Lev. "A cognitive theory without inductive learning". Behavioral and Brain Sciences 15, n.º 3 (septiembre de 1992): 446–47. http://dx.doi.org/10.1017/s0140525x00069569.
Texto completoKavic, Michael S. "Cognitive Load Theory and Learning Medicine". Photomedicine and Laser Surgery 31, n.º 8 (agosto de 2013): 357–59. http://dx.doi.org/10.1089/pho.2013.9874.
Texto completoHadi, Shahla Abdul Kadhim. "Foreign Language Learning in Light of Cognitive Learning Theory". Journal of English Language Teaching and Applied Linguistics 4, n.º 4 (20 de noviembre de 2022): 55–61. http://dx.doi.org/10.32996/jeltal.2022.4.4.7.
Texto completoKirschner, Paul A. "Cognitive load theory: implications of cognitive load theory on the design of learning". Learning and Instruction 12, n.º 1 (febrero de 2002): 1–10. http://dx.doi.org/10.1016/s0959-4752(01)00014-7.
Texto completoCardona, Mario. "Apprendere le lingue nella terza età è possibile ed è salutare. Il cervello ci dice perchè". Revista Italiano UERJ 12, n.º 2 (13 de julio de 2022): 21. http://dx.doi.org/10.12957/italianouerj.2021.67581.
Texto completoTesis sobre el tema "Cognitive learning theory"
Jankowska, Gierus Bogumila. "Learning with visual representations through cognitive load theory". Thesis, McGill University, 2011. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=104827.
Texto completoCette étude a examiné deux stratégies différentes d'apprendre à l'aide des diagrammes: le dessin de diagrammes tout en apprenant ou en apprenant sur la base des diagrammes préconstruits. Cent quatre-vingt-seize étudiants de lycée ont été aléatoirement placés dans une condition où soit ils dessinaient tout en se renseignant sur la façon dont les avions volent ou étudiaient à partir des diagrammes préconstruits. Avant l'étude, les stratégies de connaissance et d'élaboration des étudiants ont été vérifiées. Pendant l'étude sous l'une ou l'autre des conditions, les étudiants signalaient leur effort mental. Suite à cela, l'étude des étudiants est examinée sur une tâche semblable et une tâche de transfert. Cadre théorique de Cook (2006), qui combine la théorie de la connaissance antérieure et de charge cognitive sur les représentations visuelles dans l'éducation de la science, ont été employés pour analyser les résultats. Les résultats ont prouvé que l'effort mental des étudiants a augmenté sensiblement sous condition de dessin, pourtant les résultats sur le post-test étaient mitigés. En règle générale, les étudiants ont fait plus ou moins mauvais sur les mesures de post-test quand ils ont appris en traçant des diagrammes au contraire de l'utilisation des diagrammes préconstruits pour apprendre. Cependant, les étudiants ayant une faible connaissance de base ont mieux exécuté le post-test en traçant leurs propres diagrammes. Les stratégies d'élaborations n'ont pas exercé d' effet sur l'accomplissement ou l'effort mental des étudiants pour chacune des conditions.
Tsividis, Pedro A. "Theory-based learning in humans and machines". Thesis, Massachusetts Institute of Technology, 2019. https://hdl.handle.net/1721.1/121813.
Texto completoCataloged from PDF version of thesis.
Includes bibliographical references (pages 123-130).
Humans are remarkable in their ability to rapidly learn complex tasks from little experience. Recent successes in Al have produced algorithms that can perform complex tasks well in environments whose simple dynamics are known in advance, as well as models that can learn to perform expertly in unknown environments after a great amount of experience. Despite this, no current AI models are able to learn sufficiently rich and general representations so as to support rapid, human-level learning on new, complex, tasks. This thesis examines some of the epistemic practices, representations, and algorithms that we believe underlie humans' ability to quickly learn about their world and to deploy that understanding to achieve their aims. In particular, the thesis examines humans' ability to effectively query their environment for information that helps distinguish between competing hypotheses (Chapter 2); children's ability to use higher-level amodal features of data to match causes and effects (Chapter 3); and adult human rapid-learning abilities in artificial video-game environments (Chapter 4). The thesis culminates by presenting and testing a model, inspired by human inductive biases and epistemic practices, that learns to perform complex video-game tasks at human levels with human-level amounts of experience (Chapter 5). The model is an instantiation of a more general approach, Theory-Based Reinforcement Learning, which we believe can underlie the development of human-level agents that may eventually learn and act adaptively in the real world.
by Pedro A. Tsividis.
Ph. D.
Ph.D. Massachusetts Institute of Technology, Department of Brain and Cognitive Sciences
Brazas, Michael L. "Cognitive load theory and programmed instruction". [Tampa, Fla.] : University of South Florida, 2005. http://purl.fcla.edu/fcla/etd/SFE0001011.
Texto completoRitter, Samuel. "Meta-reinforcement Learning with Episodic Recall| An Integrative Theory of Reward-Driven Learning". Thesis, Princeton University, 2019. http://pqdtopen.proquest.com/#viewpdf?dispub=13420812.
Texto completoResearch on reward-driven learning has produced and substantiated theories of model-free and model-based reinforcement learning (RL), which respectively explain how humans and animals learn reflexive habits and build prospective plans. A highly developed line of work has unearthed the role of striatal dopamine in model-free learning, while the prefrontal cortex (PFC) appears to critically subserve model-based learning. The recent theory of meta-reinforcement learning (meta-RL) explained a wide array of findings by positing that the model-free dopaminergic reward prediction error trains the recurrent prefrontal network to execute arbitrary RL algorithms—including model-based RL—in its activations.
In parallel, a nascent understanding of a third reinforcement learning system is emerging: a non-parametric system that stores memory traces of individual experiences rather than aggregate statistics. Research on such episodic learning has revealed its unmistakeable traces in human behavior, developed theory to articulate algorithms underlying that behavior, and pursued the contention that the hippocampus is centrally involved. These developments lead to a set of open questions about (1) how the neural mechanisms of episodic learning relate to those underlying incremental model-free and model-based learning and (2) how the brain arbitrates among the contributions of this abundance of valuation strategies.
This thesis extends meta-RL to provide an account for episodic learning, incremental learning, and the coordination between them. In this theory of episodic meta-RL (EMRL), episodic memory reinstates activations in the prefrontal network based on contextual similarity, after passing them through a learned gating mechanism (Chapters 1 and 2). In simulation, EMRL can solve episodic contextual water maze navigation problems and episodic contextual bandit problems, including those with Omniglot class contexts and others with compositional structure (Chapter 3). Further, EMRL reproduces episodic model-based RL and its coordination with incremental model-based RL on the episodic two-step task (Vikbladh et al., 2017; Chapter 4). Chapter 5 discusses more biologically detailed extensions to EMRL, and Chapter 6 analyzes EMRL with respect to a set of recent empirical findings. Chapter 7 discusses EMRL in the context of various topics in neuroscience.
Burkes, Kate M. Erland Allen Jeff M. "Applying cognitive load theory to the design of online learning". [Denton, Tex.] : University of North Texas, 2007. http://digital.library.unt.edu/permalink/meta-dc-3698.
Texto completoBurkes, Kate M. Erland. "Applying Cognitive Load Theory to the Design of Online Learning". Thesis, University of North Texas, 2007. https://digital.library.unt.edu/ark:/67531/metadc3698/.
Texto completoBouvrie, Jacob V. "Hierarchical learning : theory with applications in speech and vision". Thesis, Massachusetts Institute of Technology, 2009. http://hdl.handle.net/1721.1/54227.
Texto completoThis electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Cataloged from student submitted PDF version of thesis.
Includes bibliographical references (p. 123-132).
Over the past two decades several hierarchical learning models have been developed and applied to a diverse range of practical tasks with much success. Little is known, however, as to why such models work as well as they do. Indeed, most are difficult to analyze, and cannot be easily characterized using the established tools from statistical learning theory. In this thesis, we study hierarchical learning architectures from two complementary perspectives: one theoretical and the other empirical. The theoretical component of the thesis centers on a mathematical framework describing a general family of hierarchical learning architectures. The primary object of interest is a recursively defined feature map, and its associated kernel. The class of models we consider exploit the fact that data in a wide variety of problems satisfy a decomposability property. Paralleling the primate visual cortex, hierarchies are assembled from alternating filtering and pooling stages that build progressively invariant representations which are simultaneously selective for increasingly complex stimuli. A goal of central importance in the study of hierarchical architectures and the cortex alike, is that of understanding quantitatively the tradeoff between invariance and selectivity, and how invariance and selectivity contribute towards providing an improved representation useful for learning from data. A reasonable expectation is that an unsupervised hierarchical representation will positively impact the sample complexity of a corresponding supervised learning task.
(cont.) We therefore analyze invariance and discrimination properties that emerge in particular instances of layered models described within our framework. A group-theoretic analysis leads to a concise set of conditions which must be met to establish invariance, as well as a constructive prescription for meeting those conditions. An information-theoretic analysis is then undertaken and seen as a means by which to characterize a model's discrimination properties. The empirical component of the thesis experimentally evaluates key assumptions built into the mathematical framework. In the case of images, we present simulations which support the hypothesis that layered architectures can reduce the sample complexity of a non-trivial learning problem. In the domain of speech, we describe a 3 localized analysis technique that leads to a noise-robust representation. The resulting biologically-motivated features are found to outperform traditional methods on a standard phonetic classification task in both clean and noisy conditions.
by Jacob V. Bouvrie.
Ph.D.
Riem, R. G. A. "Children learning to count : A social psychological reappraisal of cognitive theory". Thesis, University of Kent, 1985. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.371143.
Texto completoShon, Aaron P. "Bayesian cognitive models for imitation /". Thesis, Connect to this title online; UW restricted, 2007. http://hdl.handle.net/1773/7013.
Texto completoTobias, Cindel K. "Complex instruction giving students the education they deserve /". Online pdf file accessible through the World Wide Web, 2010. http://archives.evergreen.edu/masterstheses/Accession89-10MIT/Tobias_CKMiT2010.pdf.
Texto completoLibros sobre el tema "Cognitive learning theory"
L, Plass Jan, Moreno Roxana y Brünken Roland 1965-, eds. Cognitive load theory. New York: Cambridge University Press, 2010.
Buscar texto completoK, Estes William, Healy Alice F, Kosslyn Stephen Michael 1948- y Shiffrin Richard M, eds. From learning theory to connectionist theory. Hillsdale, N.J: L. Erlbaum, 1992.
Buscar texto completoLevine, Marvin. A Cognitive Theory of Learning. London: Routledge, 2022. http://dx.doi.org/10.4324/9781003316565.
Texto completo1942-, Flannery Daniele D., ed. Applying cognitive learning theory to adult learning. San Francisco, Calif: Jossey-Bass, 1993.
Buscar texto completoAyres, Paul L. (Paul Leslie) y Kalyuga Slava, eds. Cognitive load theory. New York: Springer, 2011.
Buscar texto completoKeefe, James W. Learning style: Theory and practice. Reston, Va: National Association of Secondary School Principals, 1987.
Buscar texto completoHeinz, Mandl y Friedrich Helmut F. 1944-, eds. Lern- und Denkstrategien: Analyse und Intervention. Göttingen: Hogrefe, 1992.
Buscar texto completoAnne, McKeough y Lupart Judy Lee, eds. Toward the practice of theory-based instruction: Current cognitive theories and their educational promise. Hillsdale, N.J: L. Erlbaum Associates, 1991.
Buscar texto completo1957-, Ackerman Phillip Lawrence, Sternberg Robert J y Glaser Robert 1921-, eds. Learning and individual differences: Advances in theory and research. New York: W.H. Freeman, 1989.
Buscar texto completoAshman, A. F. An introduction to cognitive education: Theory and applications. London: Routledge, 1997.
Buscar texto completoCapítulos de libros sobre el tema "Cognitive learning theory"
Xiaohui, Ma. "Cognitive Learning Theory". En The ECPH Encyclopedia of Psychology, 1–2. Singapore: Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-99-6000-2_1073-1.
Texto completoBusch, Bradley, Edward Watson y Ludmila Bogatchek. "Cognitive Load Theory". En Teaching & Learning Illuminated, 39–47. London: Routledge, 2023. http://dx.doi.org/10.4324/9781003334361-4.
Texto completoSweller, John. "Cognitive Load Theory". En Encyclopedia of the Sciences of Learning, 601–5. Boston, MA: Springer US, 2012. http://dx.doi.org/10.1007/978-1-4419-1428-6_446.
Texto completoBozack, Amanda. "Social Cognitive Learning Theory". En Encyclopedia of Child Behavior and Development, 1392–94. Boston, MA: Springer US, 2011. http://dx.doi.org/10.1007/978-0-387-79061-9_2715.
Texto completoWhitham, Siena, Lindsey Sterling, C. Enjey Lin y Jeffrey J. Wood. "Social Cognitive Learning Theory". En Encyclopedia of Autism Spectrum Disorders, 2884–93. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4419-1698-3_484.
Texto completoWhitham, Siena, Lindsey Sterling, Christie Enjey Lin y Jeffrey J. Wood. "Social Cognitive Learning Theory". En Encyclopedia of Autism Spectrum Disorders, 4418–27. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-319-91280-6_484.
Texto completoNugues, Pierre M. "Topics in Information Theory and Machine Learning". En Cognitive Technologies, 141–60. Cham: Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-57549-5_6.
Texto completoLevine, Marvin. "Human Discrimination Learning". En A Cognitive Theory of Learning, 213–20. London: Routledge, 2022. http://dx.doi.org/10.4324/9781003316565-27.
Texto completoFrankel, Fred, Marvin Levine y David Karpf. "Human Discrimination Learning". En A Cognitive Theory of Learning, 203–12. London: Routledge, 2022. http://dx.doi.org/10.4324/9781003316565-26.
Texto completoRestle, Frank. "A Theory of Discrimination Learning *". En A Cognitive Theory of Learning, 49–54. London: Routledge, 2022. http://dx.doi.org/10.4324/9781003316565-8.
Texto completoActas de conferencias sobre el tema "Cognitive learning theory"
Bouki, Vassiliki, Daphne Economou y Anastassia Angelopoulou. "Cognitive theory of multimedia learning and learning videos design". En the 29th ACM international conference. New York, New York, USA: ACM Press, 2011. http://dx.doi.org/10.1145/2038476.2038531.
Texto completoChau, Kien Tsong, Wan Ahmad Jaafar Wan Yahaya, Malathi Letchumanan y Por Fei Ping. "Extending Physical Multimedia Learning with Cognitive Theory of Multimedia Learning". En 2019 IEEE 4th International Conference on Signal and Image Processing (ICSIP). IEEE, 2019. http://dx.doi.org/10.1109/siprocess.2019.8868372.
Texto completoBacksanskij, O. E. y E. A. Dergacheva. "Cognitive Processes of the Brain and Learning Theory". En International Scientific Conference "Far East Con" (ISCFEC 2020). Paris, France: Atlantis Press, 2020. http://dx.doi.org/10.2991/aebmr.k.200312.010.
Texto completoMinNa, Liu. "Distributed cognitive theory: Learning concept of network era". En 2012 International Symposium on Instrumentation & Measurement, Sensor Network and Automation (IMSNA). IEEE, 2012. http://dx.doi.org/10.1109/msna.2012.6324650.
Texto completoKun, Bian, Wang Yan y Dongnan Han. "Exploring Interactive Design Strategies of Online Learning Platform Based on Cognitive Load Theory". En 14th International Conference on Applied Human Factors and Ergonomics (AHFE 2023). AHFE International, 2023. http://dx.doi.org/10.54941/ahfe1003585.
Texto completoFilipovica, Maija, Kevin Kermani Nejad, Will Greedy, Heng Wei Zhu, Jack Mellor y Rui Ponte Costa. "AI-driven cholinergic theory enables rapid and robust cortex-wide learning". En 2023 Conference on Cognitive Computational Neuroscience. Oxford, United Kingdom: Cognitive Computational Neuroscience, 2023. http://dx.doi.org/10.32470/ccn.2023.1510-0.
Texto completoMeng, Jiaying, Zhifan Wang y Zhimin Li. "Application of Cognitive Load Theory in Mobile Micro-learning". En 2016 International Conference on Management Science and Innovative Education. Paris, France: Atlantis Press, 2016. http://dx.doi.org/10.2991/msie-16.2016.110.
Texto completoSong, Shiyu. "Learning in an Online Environment: Remapping Social Cognitive Theory". En 2019 AERA Annual Meeting. Washington DC: AERA, 2019. http://dx.doi.org/10.3102/1444654.
Texto completoCody, Tyler y Peter A. Beling. "Applying Learning Systems Theory to Model Cognitive Unmanned Aerial Vehicles". En 2023 IEEE Cognitive Communications for Aerospace Applications Workshop (CCAAW). IEEE, 2023. http://dx.doi.org/10.1109/ccaaw57883.2023.10219205.
Texto completoKumar, Sanjay, Rohit Beniwal, Sudhanshu Shekhar Singh y Vipul Gupta. "Predicting Link Sign in Online Social Networks based on Social Psychology Theory and Machine Learning Techniques". En 2019 IEEE 18th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC). IEEE, 2019. http://dx.doi.org/10.1109/iccicc46617.2019.9146087.
Texto completoInformes sobre el tema "Cognitive learning theory"
McGee, Steven, Amanda Durik y Jess Zimmerman. The Impact of Text Genre on Science Learning in an Authentic Science Learning Environment. The Learning Partnership, abril de 2015. http://dx.doi.org/10.51420/conf.2015.2.
Texto completoHeckman, Stuart. Understanding insurance decisions: A review of risk management decision making, risk literacy, and racial/ethnic differences. Center for Insurance Policy and Research, enero de 2024. http://dx.doi.org/10.52227/26712.2024.
Texto completoBerlanga, Cecilia, Emma Näslund-Hadley, Enrique Fernández García y Juan Manuel Hernández Agramonte. Hybrid parental training to foster play-based early childhood development: experimental evidence from Mexico. Inter-American Development Bank, mayo de 2023. http://dx.doi.org/10.18235/0004879.
Texto completoDurik, Amanda, Steven McGee, Edward Hansen y Jennifer Duck. Comparing Middle School Students’ Responses to Narrative Versus Expository Texts on Situational and Individual Interest. The Learning Partnership, abril de 2014. http://dx.doi.org/10.51420/conf.2014.1.
Texto completoNäslund-Hadley, Emma, Michelle Koussa y Juan Manuel Hernández. Skills for Life: Stress and Brain Development in Early Childhood. Inter-American Development Bank, abril de 2021. http://dx.doi.org/10.18235/0003205.
Texto completoBarahona, Ricardo, Stefano Cassella y Kristy A. E. Jansen. Do Teams Alleviate or Exacerbate the Extrapolation Bias in the Stock Market? Madrid: Banco de España, noviembre de 2023. http://dx.doi.org/10.53479/35522.
Texto completoDyulicheva, Yulia Yu, Yekaterina A. Kosova y Aleksandr D. Uchitel. he augmented reality portal and hints usage for assisting individuals with autism spectrum disorder, anxiety and cognitive disorders. [б. в.], noviembre de 2020. http://dx.doi.org/10.31812/123456789/4412.
Texto completoBima, Luhur, Arjuni Rahmi Barasa, Shintia Revina, Niken Rarasati y Asri Yusrina. Screening Teachers in Indonesia: Does Ex-Ante Teacher Characteristics Assessment Predict Teaching Effectiveness? Research on Improving Systems of Education (RISE), marzo de 2023. http://dx.doi.org/10.35489/bsg-rise-wp_2023/134.
Texto completoOleksiuk, Vasyl P. y Olesia R. Oleksiuk. Exploring the potential of augmented reality for teaching school computer science. [б. в.], noviembre de 2020. http://dx.doi.org/10.31812/123456789/4404.
Texto completoMorkun, Volodymyr S., Сергій Олексійович Семеріков y Svitlana M. Hryshchenko. Use of the system Moodle in the formation of ecological competence of future engineers with the use of geoinformation technologies. Видавництво “CSITA”, 2016. http://dx.doi.org/10.31812/0564/718.
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