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Artykuły w czasopismach na temat "Cognitive learning theory"
Kolluru, Mythili. "Cognitive Style, Learning Preference and Performance: Theory and Empirics". International Journal of Psychosocial Rehabilitation 24, nr 4 (28.02.2020): 3678–88. http://dx.doi.org/10.37200/ijpr/v24i4/pr201481.
Pełny tekst źródłaGlenn, Cynthia Wheatley. "Cognitive Free will Learning Theory". Procedia - Social and Behavioral Sciences 97 (listopad 2013): 292–98. http://dx.doi.org/10.1016/j.sbspro.2013.10.236.
Pełny tekst źródłaBurden, Robert. "Mediated Learning Theory". School Psychology International 8, nr 1 (styczeń 1987): 59–62. http://dx.doi.org/10.1177/014303438700800108.
Pełny tekst źródłaHapps, John C. "Cognitive Learning Theory and Classroom Complexity". Research in Science & Technological Education 3, nr 2 (styczeń 1985): 159–74. http://dx.doi.org/10.1080/0263514850030109a.
Pełny tekst źródłaMcSparron, Jakob I., Anita Vanka i C. Christopher Smith. "Cognitive learning theory for clinical teaching". Clinical Teacher 16, nr 2 (23.03.2018): 96–100. http://dx.doi.org/10.1111/tct.12781.
Pełny tekst źródłaGoldfarb, Lev. "A cognitive theory without inductive learning". Behavioral and Brain Sciences 15, nr 3 (wrzesień 1992): 446–47. http://dx.doi.org/10.1017/s0140525x00069569.
Pełny tekst źródłaKavic, Michael S. "Cognitive Load Theory and Learning Medicine". Photomedicine and Laser Surgery 31, nr 8 (sierpień 2013): 357–59. http://dx.doi.org/10.1089/pho.2013.9874.
Pełny tekst źródłaHadi, Shahla Abdul Kadhim. "Foreign Language Learning in Light of Cognitive Learning Theory". Journal of English Language Teaching and Applied Linguistics 4, nr 4 (20.11.2022): 55–61. http://dx.doi.org/10.32996/jeltal.2022.4.4.7.
Pełny tekst źródłaKirschner, Paul A. "Cognitive load theory: implications of cognitive load theory on the design of learning". Learning and Instruction 12, nr 1 (luty 2002): 1–10. http://dx.doi.org/10.1016/s0959-4752(01)00014-7.
Pełny tekst źródłaCardona, Mario. "Apprendere le lingue nella terza età è possibile ed è salutare. Il cervello ci dice perchè". Revista Italiano UERJ 12, nr 2 (13.07.2022): 21. http://dx.doi.org/10.12957/italianouerj.2021.67581.
Pełny tekst źródłaRozprawy doktorskie na temat "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.
Pełny tekst źródłaCette 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.
Pełny tekst źródłaCataloged 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.
Pełny tekst źródłaRitter, 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.
Pełny tekst źródłaResearch 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.
Pełny tekst źródłaBurkes, 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/.
Pełny tekst źródłaBouvrie, Jacob V. "Hierarchical learning : theory with applications in speech and vision". Thesis, Massachusetts Institute of Technology, 2009. http://hdl.handle.net/1721.1/54227.
Pełny tekst źródłaThis 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.
Pełny tekst źródłaShon, Aaron P. "Bayesian cognitive models for imitation /". Thesis, Connect to this title online; UW restricted, 2007. http://hdl.handle.net/1773/7013.
Pełny tekst źródłaTobias, 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.
Pełny tekst źródłaKsiążki na temat "Cognitive learning theory"
L, Plass Jan, Moreno Roxana i Brünken Roland 1965-, red. Cognitive load theory. New York: Cambridge University Press, 2010.
Znajdź pełny tekst źródłaK, Estes William, Healy Alice F, Kosslyn Stephen Michael 1948- i Shiffrin Richard M, red. From learning theory to connectionist theory. Hillsdale, N.J: L. Erlbaum, 1992.
Znajdź pełny tekst źródłaLevine, Marvin. A Cognitive Theory of Learning. London: Routledge, 2022. http://dx.doi.org/10.4324/9781003316565.
Pełny tekst źródła1942-, Flannery Daniele D., red. Applying cognitive learning theory to adult learning. San Francisco, Calif: Jossey-Bass, 1993.
Znajdź pełny tekst źródłaAyres, Paul L. (Paul Leslie) i Kalyuga Slava, red. Cognitive load theory. New York: Springer, 2011.
Znajdź pełny tekst źródłaKeefe, James W. Learning style: Theory and practice. Reston, Va: National Association of Secondary School Principals, 1987.
Znajdź pełny tekst źródłaHeinz, Mandl, i Friedrich Helmut F. 1944-, red. Lern- und Denkstrategien: Analyse und Intervention. Göttingen: Hogrefe, 1992.
Znajdź pełny tekst źródłaAnne, McKeough, i Lupart Judy Lee, red. Toward the practice of theory-based instruction: Current cognitive theories and their educational promise. Hillsdale, N.J: L. Erlbaum Associates, 1991.
Znajdź pełny tekst źródła1957-, Ackerman Phillip Lawrence, Sternberg Robert J i Glaser Robert 1921-, red. Learning and individual differences: Advances in theory and research. New York: W.H. Freeman, 1989.
Znajdź pełny tekst źródłaAshman, A. F. An introduction to cognitive education: Theory and applications. London: Routledge, 1997.
Znajdź pełny tekst źródłaCzęści książek na temat "Cognitive learning theory"
Xiaohui, Ma. "Cognitive Learning Theory". W 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.
Pełny tekst źródłaBusch, Bradley, Edward Watson i Ludmila Bogatchek. "Cognitive Load Theory". W Teaching & Learning Illuminated, 39–47. London: Routledge, 2023. http://dx.doi.org/10.4324/9781003334361-4.
Pełny tekst źródłaSweller, John. "Cognitive Load Theory". W 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.
Pełny tekst źródłaBozack, Amanda. "Social Cognitive Learning Theory". W 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.
Pełny tekst źródłaWhitham, Siena, Lindsey Sterling, C. Enjey Lin i Jeffrey J. Wood. "Social Cognitive Learning Theory". W 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.
Pełny tekst źródłaWhitham, Siena, Lindsey Sterling, Christie Enjey Lin i Jeffrey J. Wood. "Social Cognitive Learning Theory". W Encyclopedia of Autism Spectrum Disorders, 4418–27. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-319-91280-6_484.
Pełny tekst źródłaNugues, Pierre M. "Topics in Information Theory and Machine Learning". W Cognitive Technologies, 141–60. Cham: Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-57549-5_6.
Pełny tekst źródłaLevine, Marvin. "Human Discrimination Learning". W A Cognitive Theory of Learning, 213–20. London: Routledge, 2022. http://dx.doi.org/10.4324/9781003316565-27.
Pełny tekst źródłaFrankel, Fred, Marvin Levine i David Karpf. "Human Discrimination Learning". W A Cognitive Theory of Learning, 203–12. London: Routledge, 2022. http://dx.doi.org/10.4324/9781003316565-26.
Pełny tekst źródłaRestle, Frank. "A Theory of Discrimination Learning *". W A Cognitive Theory of Learning, 49–54. London: Routledge, 2022. http://dx.doi.org/10.4324/9781003316565-8.
Pełny tekst źródłaStreszczenia konferencji na temat "Cognitive learning theory"
Bouki, Vassiliki, Daphne Economou i Anastassia Angelopoulou. "Cognitive theory of multimedia learning and learning videos design". W the 29th ACM international conference. New York, New York, USA: ACM Press, 2011. http://dx.doi.org/10.1145/2038476.2038531.
Pełny tekst źródłaChau, Kien Tsong, Wan Ahmad Jaafar Wan Yahaya, Malathi Letchumanan i Por Fei Ping. "Extending Physical Multimedia Learning with Cognitive Theory of Multimedia Learning". W 2019 IEEE 4th International Conference on Signal and Image Processing (ICSIP). IEEE, 2019. http://dx.doi.org/10.1109/siprocess.2019.8868372.
Pełny tekst źródłaBacksanskij, O. E., i E. A. Dergacheva. "Cognitive Processes of the Brain and Learning Theory". W International Scientific Conference "Far East Con" (ISCFEC 2020). Paris, France: Atlantis Press, 2020. http://dx.doi.org/10.2991/aebmr.k.200312.010.
Pełny tekst źródłaMinNa, Liu. "Distributed cognitive theory: Learning concept of network era". W 2012 International Symposium on Instrumentation & Measurement, Sensor Network and Automation (IMSNA). IEEE, 2012. http://dx.doi.org/10.1109/msna.2012.6324650.
Pełny tekst źródłaKun, Bian, Wang Yan i Dongnan Han. "Exploring Interactive Design Strategies of Online Learning Platform Based on Cognitive Load Theory". W 14th International Conference on Applied Human Factors and Ergonomics (AHFE 2023). AHFE International, 2023. http://dx.doi.org/10.54941/ahfe1003585.
Pełny tekst źródłaFilipovica, Maija, Kevin Kermani Nejad, Will Greedy, Heng Wei Zhu, Jack Mellor i Rui Ponte Costa. "AI-driven cholinergic theory enables rapid and robust cortex-wide learning". W 2023 Conference on Cognitive Computational Neuroscience. Oxford, United Kingdom: Cognitive Computational Neuroscience, 2023. http://dx.doi.org/10.32470/ccn.2023.1510-0.
Pełny tekst źródłaMeng, Jiaying, Zhifan Wang i Zhimin Li. "Application of Cognitive Load Theory in Mobile Micro-learning". W 2016 International Conference on Management Science and Innovative Education. Paris, France: Atlantis Press, 2016. http://dx.doi.org/10.2991/msie-16.2016.110.
Pełny tekst źródłaSong, Shiyu. "Learning in an Online Environment: Remapping Social Cognitive Theory". W 2019 AERA Annual Meeting. Washington DC: AERA, 2019. http://dx.doi.org/10.3102/1444654.
Pełny tekst źródłaCody, Tyler, i Peter A. Beling. "Applying Learning Systems Theory to Model Cognitive Unmanned Aerial Vehicles". W 2023 IEEE Cognitive Communications for Aerospace Applications Workshop (CCAAW). IEEE, 2023. http://dx.doi.org/10.1109/ccaaw57883.2023.10219205.
Pełny tekst źródłaKumar, Sanjay, Rohit Beniwal, Sudhanshu Shekhar Singh i Vipul Gupta. "Predicting Link Sign in Online Social Networks based on Social Psychology Theory and Machine Learning Techniques". W 2019 IEEE 18th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC). IEEE, 2019. http://dx.doi.org/10.1109/iccicc46617.2019.9146087.
Pełny tekst źródłaRaporty organizacyjne na temat "Cognitive learning theory"
McGee, Steven, Amanda Durik i Jess Zimmerman. The Impact of Text Genre on Science Learning in an Authentic Science Learning Environment. The Learning Partnership, kwiecień 2015. http://dx.doi.org/10.51420/conf.2015.2.
Pełny tekst źródłaHeckman, Stuart. Understanding insurance decisions: A review of risk management decision making, risk literacy, and racial/ethnic differences. Center for Insurance Policy and Research, styczeń 2024. http://dx.doi.org/10.52227/26712.2024.
Pełny tekst źródłaBerlanga, Cecilia, Emma Näslund-Hadley, Enrique Fernández García i Juan Manuel Hernández Agramonte. Hybrid parental training to foster play-based early childhood development: experimental evidence from Mexico. Inter-American Development Bank, maj 2023. http://dx.doi.org/10.18235/0004879.
Pełny tekst źródłaDurik, Amanda, Steven McGee, Edward Hansen i Jennifer Duck. Comparing Middle School Students’ Responses to Narrative Versus Expository Texts on Situational and Individual Interest. The Learning Partnership, kwiecień 2014. http://dx.doi.org/10.51420/conf.2014.1.
Pełny tekst źródłaNäslund-Hadley, Emma, Michelle Koussa i Juan Manuel Hernández. Skills for Life: Stress and Brain Development in Early Childhood. Inter-American Development Bank, kwiecień 2021. http://dx.doi.org/10.18235/0003205.
Pełny tekst źródłaBarahona, Ricardo, Stefano Cassella i Kristy A. E. Jansen. Do Teams Alleviate or Exacerbate the Extrapolation Bias in the Stock Market? Madrid: Banco de España, listopad 2023. http://dx.doi.org/10.53479/35522.
Pełny tekst źródłaDyulicheva, Yulia Yu, Yekaterina A. Kosova i Aleksandr D. Uchitel. he augmented reality portal and hints usage for assisting individuals with autism spectrum disorder, anxiety and cognitive disorders. [б. в.], listopad 2020. http://dx.doi.org/10.31812/123456789/4412.
Pełny tekst źródłaBima, Luhur, Arjuni Rahmi Barasa, Shintia Revina, Niken Rarasati i Asri Yusrina. Screening Teachers in Indonesia: Does Ex-Ante Teacher Characteristics Assessment Predict Teaching Effectiveness? Research on Improving Systems of Education (RISE), marzec 2023. http://dx.doi.org/10.35489/bsg-rise-wp_2023/134.
Pełny tekst źródłaOleksiuk, Vasyl P., i Olesia R. Oleksiuk. Exploring the potential of augmented reality for teaching school computer science. [б. в.], listopad 2020. http://dx.doi.org/10.31812/123456789/4404.
Pełny tekst źródłaMorkun, Volodymyr S., Сергій Олексійович Семеріков i 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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