Готові списки джерел за темами / Cognitive learning theory

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Добірка наукової літератури з теми "Cognitive learning theory"

Автор: Grafiati
Опубліковано: 4 червня 2021
Оновлено: 9 червня 2025

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Статті в журналах з теми "Cognitive learning theory"

1

Kolluru, Mythili. "Cognitive Style, Learning Preference and Performance: Theory and Empirics." International Journal of Psychosocial Rehabilitation 24, no. 4 (February 28, 2020): 3678–88. http://dx.doi.org/10.37200/ijpr/v24i4/pr201481.

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2

Glenn, Cynthia Wheatley. "Cognitive Free will Learning Theory." Procedia - Social and Behavioral Sciences 97 (November 2013): 292–98. http://dx.doi.org/10.1016/j.sbspro.2013.10.236.

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3

Burden, Robert. "Mediated Learning Theory." School Psychology International 8, no. 1 (January 1987): 59–62. http://dx.doi.org/10.1177/014303438700800108.

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The growing spread of interest in Feuerstein's theory of mediated learning as an essential prerequisite of adequate cognitive development and the developing worldwide enthusiasm amongst psychologists and special educators for the theory's two practical offspring, the learning potential assessment device (LPAD) and instrumental enrichment, was probably initiated by the publication of two key texts (Feuerstein, Rand and Hoffman, 1979; Feuerstein et al, 1980). Although the Israeli team of researchers and special educators had been developing and disseminating their ideas for some two decades prior to these publications, it is only during the 1980s that mediated learning has really struck a responsive chord amongst those who continue to believe that all children can be taught to be more effective learners, that there is a key to remediating cognitive defects and that psychology has a part to play in helping schools to achieve this.
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4

Happs, John C. "Cognitive Learning Theory and Classroom Complexity." Research in Science & Technological Education 3, no. 2 (January 1985): 159–74. http://dx.doi.org/10.1080/0263514850030109a.

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McSparron, Jakob I., Anita Vanka, and C. Christopher Smith. "Cognitive learning theory for clinical teaching." Clinical Teacher 16, no. 2 (March 23, 2018): 96–100. http://dx.doi.org/10.1111/tct.12781.

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Goldfarb, Lev. "A cognitive theory without inductive learning." Behavioral and Brain Sciences 15, no. 3 (September 1992): 446–47. http://dx.doi.org/10.1017/s0140525x00069569.

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7

Kavic, Michael S. "Cognitive Load Theory and Learning Medicine." Photomedicine and Laser Surgery 31, no. 8 (August 2013): 357–59. http://dx.doi.org/10.1089/pho.2013.9874.

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8

Hadi, Shahla Abdul Kadhim. "Foreign Language Learning in Light of Cognitive Learning Theory." Journal of English Language Teaching and Applied Linguistics 4, no. 4 (November 20, 2022): 55–61. http://dx.doi.org/10.32996/jeltal.2022.4.4.7.

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Анотація:
Language, with a capital initial, indicates the human system of verbal communication, which has a lot of variations represented by various languages spoken in the world. All languages involve the same mechanisms that govern their patterning because all humans have the same architecture of the cognitive system and follow the same cognitive learning principles in acquiring knowledge. While the cognitive processing mechanisms are unconscious and automatic in first language acquisition, they are effortful and can impose load on the cognitive system of the EFL learners due to factors internal to the texture of languages and some other external factors related to the cultures of individuals; which commits the learner to process multiple resources of information simultaneously before being able to schematize the new knowledge related to the target language. Accordingly, this paper discusses foreign language learning in light of cognitive learning theory with the aim of explaining why it can be hard on the EFL learners’ part to acquire the target language perfectly.
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9

Freitas, Sandra Ferreira, and Christiane Kleinübing Godoi. "A APRENDIZAGEM ORGANIZACIONAL SOB A PERSPECTIVA SÓCIO-COGNITIVA: CONTRIBUIÇÕES DE LEWIN, BANDURA E GIDDENS." Revista de Negócios 13, no. 4 (February 11, 2009): 40. http://dx.doi.org/10.7867/1980-4431.2008v13n4p40-55.

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This article seeks to establish the transfer of contributions of socio-cognitive learning theories to the sphere of organizational learning. The central argument is the idea that social cognition explains organizational learning more adequately than the fragmented studies of learning derived from the organizational field. Within the socio-cognitive perspective, organizational learning is understood as the result of a reciprocal exchange between socio-cognitive constructs and organizational culture. The understanding of organizational learning requires consideration of the social aspects of learning, and is based on theories capable of interconnecting individual processes, the functioning of the groups, and social relations. Among the learning theories that consider the social context, we elect the analysis and transfer of the following theories, to the organizational sphere: a) Kurt Lewin’s field theory (and his influence on Dewey); b) Bandura’s cognitive social learning theory (and the influence of the attribution theory); and Giddens’ theory of structuration.
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Kirschner, Paul A. "Cognitive load theory: implications of cognitive load theory on the design of learning." Learning and Instruction 12, no. 1 (February 2002): 1–10. http://dx.doi.org/10.1016/s0959-4752(01)00014-7.

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Дисертації з теми "Cognitive learning theory"

1

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.

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This study examined two different strategies of learning with diagrams: drawing diagrams while learning or learning from pre-constructed diagrams. One hundred ninety six junior high school students were randomly placed in a condition either to draw while learning about how airplanes fly or to study from pre-constructed diagrams. Before the learning, students' prior knowledge and elaboration strategies were measured. During learning in either condition, students reported their mental effort. Afterwards, students' learning was tested on both a similar task and transfer task. Cook's (2006) theoretical framework, which combines prior knowledge and cognitive load theory on visual representations in science education, was used to analyze the results. Results showed that students' mental effort significantly increased in the drawing condition, yet results on the posttest were mixed. Students did not do better, and sometimes did worse, on the posttest measures when they learned by drawing diagrams versus using pre-constructed diagrams to learn. The exception was that students with low initial prior knowledge did do better. Elaborations strategies did not have an effect on students' achievement or mental effort in either condition.<br>Cette 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.
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2

Tsividis, Pedro A. "Theory-based learning in humans and machines." Thesis, Massachusetts Institute of Technology, 2019. https://hdl.handle.net/1721.1/121813.

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Thesis: Ph. D., Massachusetts Institute of Technology, Department of Brain and Cognitive Sciences, 2019<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (pages 123-130).<br>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.<br>by Pedro A. Tsividis.<br>Ph. D.<br>Ph.D. Massachusetts Institute of Technology, Department of Brain and Cognitive Sciences
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3

Brazas, Michael L. "Cognitive load theory and programmed instruction." [Tampa, Fla.] : University of South Florida, 2005. http://purl.fcla.edu/fcla/etd/SFE0001011.

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4

Ritter, 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.

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<p> Research 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&mdash;including model-based RL&mdash;in its activations. </p><p> 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 <i>episodic learning</i> 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. </p><p> 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.</p><p>
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5

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.

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Burkes, 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/.

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The purpose of the study was to investigate the application of cognitive load theory to the design of online instruction. Students in three different courses (N = 146) were measured on both learning performance and perceptions of mental effort to see if there were any statistically significant differences. The study utilized a quasi-experimental posttest-only control group design contrasting modified and unmodified instructional lessons. Both groups were given a posttest to measure knowledge gained from the lesson (cognitive domain of learning) and perceptions of mental effort involved. Independent samples t-tests were used to compare the mean performance scores of the treatment groups (i.e. the sections using redesigned materials) versus the control groups for all three courses. Cohen's d was also computed to determine effect size. Mental effort scores were similarly compared for each group on the overall cognitive load score, for a total of six data points in the study. Of the four hypotheses examined, three (H1, H2, H4) found no statistically significant difference between the experimental and control groups. Negative significance was found between the experimental and control group on the effect of modality (H3). On measures of cognitive load, no statistically significant differences were found.
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7

Bouvrie, Jacob V. "Hierarchical learning : theory with applications in speech and vision." Thesis, Massachusetts Institute of Technology, 2009. http://hdl.handle.net/1721.1/54227.

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Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Brain and Cognitive Sciences, 2009.<br>This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.<br>Cataloged from student submitted PDF version of thesis.<br>Includes bibliographical references (p. 123-132).<br>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.<br>(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.<br>by Jacob V. Bouvrie.<br>Ph.D.
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8

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.

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Shon, Aaron P. "Bayesian cognitive models for imitation /." Thesis, Connect to this title online; UW restricted, 2007. http://hdl.handle.net/1773/7013.

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Tobias, 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.

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Книги з теми "Cognitive learning theory"

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L, Plass Jan, Moreno Roxana, and Brünken Roland 1965-, eds. Cognitive load theory. New York: Cambridge University Press, 2010.

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2

K, Estes William, Healy Alice F, Kosslyn Stephen Michael 1948-, and Shiffrin Richard M, eds. From learning theory to connectionist theory. Hillsdale, N.J: L. Erlbaum, 1992.

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3

Levine, Marvin. A Cognitive Theory of Learning. London: Routledge, 2022. http://dx.doi.org/10.4324/9781003316565.

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4

1942-, Flannery Daniele D., ed. Applying cognitive learning theory to adult learning. San Francisco, Calif: Jossey-Bass, 1993.

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5

Ayres, Paul L. (Paul Leslie) and Kalyuga Slava, eds. Cognitive load theory. New York: Springer, 2011.

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6

Keefe, James W. Learning style: Theory and practice. Reston, Va: National Association of Secondary School Principals, 1987.

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7

Heinz, Mandl, and Friedrich Helmut F. 1944-, eds. Lern- und Denkstrategien: Analyse und Intervention. Göttingen: Hogrefe, 1992.

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8

Al-Thani, Noora J., and Zubair Ahmad. Teaching and Learning with Research Cognitive Theory. Cham: Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-87544-1.

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Anne, McKeough, and 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.

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10

1957-, Ackerman Phillip Lawrence, Sternberg Robert J, and Glaser Robert 1921-, eds. Learning and individual differences: Advances in theory and research. New York: W.H. Freeman, 1989.

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Частини книг з теми "Cognitive learning theory"

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Xiaohui, Ma. "Cognitive Learning Theory." In 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.

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Xiaohui, Ma. "Cognitive Learning Theory." In The ECPH Encyclopedia of Psychology, 239–40. Singapore: Springer Nature Singapore, 2024. https://doi.org/10.1007/978-981-97-7874-4_1073.

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Busch, Bradley, Edward Watson, and Ludmila Bogatchek. "Cognitive Load Theory." In Teaching & Learning Illuminated, 39–47. London: Routledge, 2023. http://dx.doi.org/10.4324/9781003334361-4.

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Sweller, John. "Cognitive Load Theory." In 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.

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Bozack, Amanda. "Social Cognitive Learning Theory." In 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.

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Whitham, Siena, Lindsey Sterling, C. Enjey Lin, and Jeffrey J. Wood. "Social Cognitive Learning Theory." In 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.

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Whitham, Siena, Lindsey Sterling, Christie Enjey Lin, and Jeffrey J. Wood. "Social Cognitive Learning Theory." In Encyclopedia of Autism Spectrum Disorders, 4418–27. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-319-91280-6_484.

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Kossak, Hans-Christian. "Learning Theory Paradigms." In Cognitive-behavioral Psychotherapy for Anxiety, 77–91. Berlin, Heidelberg: Springer Berlin Heidelberg, 2024. http://dx.doi.org/10.1007/978-3-662-69099-4_6.

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Nugues, Pierre M. "Topics in Information Theory and Machine Learning." In Cognitive Technologies, 141–60. Cham: Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-57549-5_6.

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Levine, Marvin. "Human Discrimination Learning." In A Cognitive Theory of Learning, 213–20. London: Routledge, 2022. http://dx.doi.org/10.4324/9781003316565-27.

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Тези доповідей конференцій з теми "Cognitive learning theory"

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Yeo, Gerard, Shaz Furniturewala, and Kokil Jaidka. "Beyond Text: Leveraging Multi-Task Learning and Cognitive Appraisal Theory for Post-Purchase Intention Analysis." In Findings of the Association for Computational Linguistics ACL 2024, 12353–60. Stroudsburg, PA, USA: Association for Computational Linguistics, 2024. http://dx.doi.org/10.18653/v1/2024.findings-acl.734.

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Wang, Jing, Yong Jun He, C. L. Philip Chen, Xiping Jia, Zhiyong Lin, and Huiming Zhao. "An Enhanced Broad Learning System with Mean Time Series Difference for Aided Diagnosis of Mild Cognitive Impairment." In 2024 International Conference on Fuzzy Theory and Its Applications (iFUZZY), 01–05. IEEE, 2024. http://dx.doi.org/10.1109/ifuzzy63051.2024.10661370.

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Bouki, Vassiliki, Daphne Economou, and Anastassia Angelopoulou. "Cognitive theory of multimedia learning and learning videos design." In the 29th ACM international conference. New York, New York, USA: ACM Press, 2011. http://dx.doi.org/10.1145/2038476.2038531.

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Chau, Kien Tsong, Wan Ahmad Jaafar Wan Yahaya, Malathi Letchumanan, and Por Fei Ping. "Extending Physical Multimedia Learning with Cognitive Theory of Multimedia Learning." In 2019 IEEE 4th International Conference on Signal and Image Processing (ICSIP). IEEE, 2019. http://dx.doi.org/10.1109/siprocess.2019.8868372.

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Backsanskij, O. E., and E. A. Dergacheva. "Cognitive Processes of the Brain and Learning Theory." In International Scientific Conference "Far East Con" (ISCFEC 2020). Paris, France: Atlantis Press, 2020. http://dx.doi.org/10.2991/aebmr.k.200312.010.

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MinNa, Liu. "Distributed cognitive theory: Learning concept of network era." In 2012 International Symposium on Instrumentation & Measurement, Sensor Network and Automation (IMSNA). IEEE, 2012. http://dx.doi.org/10.1109/msna.2012.6324650.

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Kun, Bian, Wang Yan, and Dongnan Han. "Exploring Interactive Design Strategies of Online Learning Platform Based on Cognitive Load Theory." In 14th International Conference on Applied Human Factors and Ergonomics (AHFE 2023). AHFE International, 2023. http://dx.doi.org/10.54941/ahfe1003585.

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In recent years, online learning has been increasingly popular due to its convenience and accessibility. To improve the quality of online learning, it is essential to understand the learners' cognitive load during online learning interaction. Cognitive load theory and teaching interaction hierarchy theory are employed to explore the impact of learners' cognitive load during online learning interaction. Based on these theories, this study utilizes EEG technology and subjective measurement to measure the cognitive load of learners' operational interaction and information interaction during online learning interaction. Six typical tasks were studied, including login, search, browse, share, and discuss. The results demonstrate that the login and search tasks have a higher cognitive load and the browse and share tasks have a lower cognitive load among the six typical tasks, virtual reality learning environments have a lower cognitive load than online learning environments. Therefore, by correctly identifying the cognitive load of tasks in operational and information interaction, optimization strategies can help to reduce the cognitive load of learners during online learning interaction and improve the quality of online learning.
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COMAN, Nicolaie. "Cognitive perspectives in learning – from theory to practice in education." In "Instruire prin cercetare pentru o societate prosperă", conferinţă ştiinţifico-practică internaţională, 211–15. Ion Creangă Pedagogical State University, 2024. https://doi.org/10.46727/c.v2.16-17-05-2024.p211-215.

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The article explores the learning process from a cognitive perspective, highlighting how the human mind works in processing new information and solving school tasks. Information processing theory and the information processing model are at the heart of the analysis, providing a detailed understanding of how information is encoded, stored, and interpreted in memory. Furthermore, recommendations for teachers are presented, starting from the influence of external stimuli on the learning process to the optimal strategies for stimulating long-term memory and short-term memory. The appreciation of cognitive approaches in education is highlighted as essential for optimizing the teaching-learning process.
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Filipovica, Maija, Kevin Kermani Nejad, Will Greedy, Heng Wei Zhu, Jack Mellor, and Rui Ponte Costa. "AI-driven cholinergic theory enables rapid and robust cortex-wide learning." In 2023 Conference on Cognitive Computational Neuroscience. Oxford, United Kingdom: Cognitive Computational Neuroscience, 2023. http://dx.doi.org/10.32470/ccn.2023.1510-0.

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Meng, Jiaying, Zhifan Wang, and Zhimin Li. "Application of Cognitive Load Theory in Mobile Micro-learning." In 2016 International Conference on Management Science and Innovative Education. Paris, France: Atlantis Press, 2016. http://dx.doi.org/10.2991/msie-16.2016.110.

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Звіти організацій з теми "Cognitive learning theory"

1

Pasupuleti, Murali Krishna. Quantum Cognition: Modeling Decision-Making with Quantum Theory. National Education Services, March 2025. https://doi.org/10.62311/nesx/rrvi225.

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Abstract Quantum cognition applies quantum probability theory and mathematical principles from quantum mechanics to model human decision-making, reasoning, and cognitive processes beyond the constraints of classical probability models. Traditional decision theories, such as expected utility theory and Bayesian inference, struggle to explain context-dependent reasoning, preference reversals, order effects, and cognitive biases observed in human behavior. By incorporating superposition, interference, and entanglement, quantum cognitive models offer a probabilistic framework that better accounts for uncertainty, ambiguity, and adaptive decision-making in complex environments. This research explores the foundations of quantum cognition, its empirical validation in behavioral experiments and neuroscience, and its applications in artificial intelligence (AI), behavioral economics, and decision sciences. Additionally, it examines how quantum-inspired AI models enhance predictive analytics, machine learning algorithms, and human-computer interaction. The study also addresses challenges related to mathematical complexity, cognitive interpretation, and the potential link between quantum mechanics and brain function, providing a comprehensive framework for the integration of quantum cognition into decision science and AI-driven cognitive computing. Keywords Quantum cognition, quantum probability, decision-making models, cognitive science, superposition in cognition, interference effects, entanglement in decision-making, probabilistic reasoning, preference reversals, cognitive biases, order effects, quantum-inspired AI, behavioral economics, neural quantum theory, artificial intelligence, cognitive neuroscience, human-computer interaction, quantum probability in psychology, quantum decision theory, uncertainty modeling, predictive analytics, quantum computing in cognition.
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Pasupuleti, Murali Krishna. Neural Computation and Learning Theory: Expressivity, Dynamics, and Biologically Inspired AI. National Education Services, March 2025. https://doi.org/10.62311/nesx/rriv425.

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Abstract: Neural computation and learning theory provide the foundational principles for understanding how artificial and biological neural networks encode, process, and learn from data. This research explores expressivity, computational dynamics, and biologically inspired AI, focusing on theoretical expressivity limits, infinite-width neural networks, recurrent and spiking neural networks, attractor models, and synaptic plasticity. The study investigates mathematical models of function approximation, kernel methods, dynamical systems, and stability properties to assess the generalization capabilities of deep learning architectures. Additionally, it explores biologically plausible learning mechanisms such as Hebbian learning, spike-timing-dependent plasticity (STDP), and neuromodulation, drawing insights from neuroscience and cognitive computing. The role of spiking neural networks (SNNs) and neuromorphic computing in low-power AI and real-time decision-making is also analyzed, with applications in robotics, brain-computer interfaces, edge AI, and cognitive computing. Case studies highlight the industrial adoption of biologically inspired AI, focusing on adaptive neural controllers, neuromorphic vision, and memory-based architectures. This research underscores the importance of integrating theoretical learning principles with biologically motivated AI models to develop more interpretable, generalizable, and scalable intelligent systems. Keywords Neural computation, learning theory, expressivity, deep learning, recurrent neural networks, spiking neural networks, biologically inspired AI, infinite-width networks, kernel methods, attractor networks, synaptic plasticity, STDP, neuromodulation, cognitive computing, dynamical systems, function approximation, generalization, AI stability, neuromorphic computing, robotics, brain-computer interfaces, edge AI, biologically plausible learning.
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3

Aw, Guat Poh, Kar Chun Tan, and Kwee Hua Lim. Teachers as adult learners-effecting professional development and teacher change in primary Chinese language teaching and learning through variation theory and multi-perspectival reflective dialogue. National Institute of Education, Nanyang Technological University, Singapore, 2024. https://doi.org/10.32658/10497/27415.

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The present study serves as an intervention study which adopts the Variation Theory-Multiple Perspectival Reflective Dialogic Professional Development Model (VT-MPR dialogic PD Model developed by the researcher) as a systematic framework to investigate aspects of Cognitive Dissonance (CD) experienced by primary school Chinese Language (CL) teachers in Singapore.
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Scoular, Claire, Ian Teo, Jonathan Heard, and Adam Wardell. Self-regulation: Skill development framework. Australian Council for Educational Research, January 2025. https://doi.org/10.37517/978-1-74286-750-2.

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This self-regulation skill development framework was developed to address the challenges associated with teaching and assessing self-regulation. It provides a general definition of self-regulation and describes this construct as it applies in classroom-based learning. This framework synthesises and harmonises existing theory and research on self-regulation. It outlines self-regulation processes along prescribed strands and aspects that are informed by evidence. The strands contained within the framework are 1. Cognitive regulating; 2. Behavioural regulating; and 3. Emotional regulating. The strands are further broken down into aspects to inform teaching and assessment.
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McGee, Steven, Amanda Durik, and Jess Zimmerman. The Impact of Text Genre on Science Learning in an Authentic Science Learning Environment. The Learning Partnership, April 2015. http://dx.doi.org/10.51420/conf.2015.2.

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A gap exists between research on learning and research on interest. Cognitive researchers rarely consider motivational processes, and interest researchers rarely consider cognitive process. However, it is essential to consider both since achievement and interest are in fact intertwined. In this paper we (1) discuss a theoretical model that intertwines cognitive and interest development, (2) describe how that model informed the development of educational materials, and (3) report on the results of the cognitive components of a randomized research study examining the impact of text genre on learning and interest. In our prior analyses, we examined the effects of text characteristics (i.e., narrative or expository genre) on situational interest. We found that students with higher levels of prior individual interest preferred the narrative versions of text whereas students with lower levels of prior individual interest preferred the expository versions of text. In this paper, we examine the impact of text characteristics on student learning. The results of this research showed that contrary to prior research, there was no significant difference in comprehension based on text characteristics. These results provide evidence that is possible to differentiate instruction based students' prior interest without sacrificing learning outcomes.
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Hampton, Sarah, Kip Glazer, Dalila Dragnić-Cindrić, and Judi Fusco. Social Regulation of Learning. Digital Promise, May 2025. https://doi.org/10.51388/20.500.12265/249.

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Social regulation of learning is a critical component of successful collaborative learning, encompassing self-regulation, co-regulation, and socially shared regulation of learning. This primer explores these components and discusses how they help students navigate social, cognitive, and emotional challenges within collaborative settings. The primer also highlights the teacher's role in fostering regulation skills, the importance of a positive classroom climate, and the need for students to be given time in groups without their teacher to develop their shared regulation skills. Further, it addresses challenges in implementing social regulation of learning and provides practical indicators and strategies for educators to enhance collaborative learning experiences. The paper provides newer research through the eyes of practitioners and researchers together who are working towards a deeper understanding and application of social regulation of learning in classrooms.
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Tay, Hui Yong, and Norman Kiak Nam Kee. Assessments for learning in inclusive classrooms. National Institute of Education, Nanyang Technological University, Singapore, 2020. https://doi.org/10.32658/10497/23254.

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This study builds upon recent research into Assessment for Learning (AfL) practices in Singapore secondary schools (Brown, Deneen, Fulmer, Leong, Tan, &amp; Tay, 2017 April). It aims to extend our understanding of certain AfL competencies highlighted in the latter study in a more specific context of an inclusive classroom, particularly mainstream school classes with students with Autism Spectrum Disorder (ASD). The incidence of ASD in Singapore is one in 150 children, higher than World Health Organisation's global figure of one in 160 (Ng, 2017). Students with ASD have the cognitive abilities to benefit from the national curriculum, but face challenges in social communication and socialising. They find it difficult to read the intentions of others or see the bigger picture because they tend to use their superior detail-focused cognitive style (Engeland &amp; Buitelaar, 2008; Happe &amp; Frith, 2006). It is challenging for them to benefit from the classroom dialogue in which teachers ask questions to elicit evidence of learning as well as give feedback to advance the students' understanding. However, there is currently no study to help us understand the phenomenon in Singapore schools. To help bridge this gap, this exploratory qualitative study seeks to understand, in the context of an inclusive classroom, a) How would effective questioning and teacher feedback look like, separately and when used together? b) What do ASD children consider to be effective questioning techniques and teacher feedback? Participants will include 6 teachers from schools (both primary and secondary) trained in special needs (TSN) will be observed during a lesson in a class which has ASD student. Both the teacher and the student will be interviewed separately after the lesson. The findings of this study will extend our current understanding of AfL in the context of inclusive education where every child is valued and enabled to learn (UNESCO, 2005).
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Heckman, Stuart. Understanding insurance decisions: A review of risk management decision making, risk literacy, and racial/ethnic differences. Center for Insurance Policy and Research, January 2024. http://dx.doi.org/10.52227/26712.2024.

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The racial/ethnic wealth gap is a stunning feature of U.S. household finances. Although the causes of the gap are complex, it is important that researchers investigate disparities between racial/ethnic groups in household financial management areas. We posit that first understanding insurance decisions as a critical component of overall household financial management is an important avenue for further understanding factors that may perpetuate or reduce the racial wealth gap. Moreover, risk management, including the purchase and use of insurance products, is a key yet challenging area for household financial management. Therefore, this literature review focuses on research relevant to three main questions: 1) How do consumers make risk management decisions? 2) What key skills are required to make risk management decisions (with a focus on literacy and numeracy skills)? 3) Do these skills vary between racial/ethnic groups? Regarding the first question, we find that consumers are prone to errors when making decisions involving risk, but research shows that decisions can be improved. Skilled Decision Theory (SDT) highlights that cognitive ability plays less of a central role in decision-making and that decision-making is more of an acquired skill. Consequently, learning comprehension and confidence play a crucial role in the decision-making process. In terms of the second question and the skills needed to make appropriate risk management decisions, the literature suggests that insurance literacy, not necessarily financial literacy, as well as numeracy skills are likely to be critical prerequisites to good insurance choices. In particular, the importance of statistical numeracy in decision-making cannot be overstated. Finally for our third question, our review indicates that there is a relatively limited number of available studies focusing on racial/ethnic differences in risk management decisions and skills. While some studies find differences between racial/ethnic groups in various measures of financial literacy, the findings are overall mixed and, therefore, inconclusive. Researchers should verify if there are, in fact, differences or if the differences are due to other factors that vary by racial/ethnic category.
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Berlanga, Cecilia, Emma Näslund-Hadley, Enrique Fernández García, and Juan Manuel Hernández Agramonte. Hybrid parental training to foster play-based early childhood development: experimental evidence from Mexico. Inter-American Development Bank, May 2023. http://dx.doi.org/10.18235/0004879.

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Play during early childhood is key to stimulating childrens physical, social, emotional and cognitive development; it promotes their imagination and creativity, improves their problem-solving skills and enhances their learning readiness by providing the foundations to build skills later in their lives. Parental engagement in play-based learning at home is one of the behaviors most consistently associated with positive child development. However, it is concerning that levels of parental engagement in play activities have been found to be lower in low-resourced settings. Additionally, research on play-based learning is largely limited to high-income countries and little is known about the use of hybrid interventions that promote play-based learning at home. This study uses an experimental design to estimate the effects of a hybrid large-scale parental program to promote play-based learning in the state of Morelos, Mexico. We found a positive impact on parental investment, as caregivers of the treatment group had a FCI 0.13 SD higher than the control group. The treatment group performed the following activities more often than the control group: reading books /looking at pictures (0.12 SD), singing songs (0.11 SD), and playing with toys (0.17 SD), which incentivize learning, emotional and cognitive skills development in children. The study also found a significant effect of 0.19 SD on the CDC index for those caregivers who invested less than the median FCI at the baseline. Our findings support the importance of parental training for increased quality and time of caregiver investments in play activities, which lead to improved child outcomes, especially among children in households with the lowest levels of caregiver investment at baseline.
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Durik, Amanda, Steven McGee, Edward Hansen, and Jennifer Duck. Comparing Middle School Students’ Responses to Narrative Versus Expository Texts on Situational and Individual Interest. The Learning Partnership, April 2014. http://dx.doi.org/10.51420/conf.2014.1.

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This project examined the effects of text genre on both situational and individual interest. Middle school students completed a three-session web-based learning module in the domain of ecology wherein they were randomly assigned to either narrative or expository readings that were matched on key idea units and other variables. Students reported individual interest in ecology on the day before and after their exposure to the module. Affective and cognitive situational interest was measured after the readings on each day of the module. The results showed that expository readings were perceived as more helpful for learning than were narrative readings, but this varied somewhat by initial individual interest. Although the narrative versions did not facilitate situational interest, there was a small effect on individual interest suggesting that learners exposed to narrative readings came to perceive the domain of ecology as a more meaningful discipline than did those exposed to expository readings.
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