Gotowe bibliografie tematyczne / Cognitive learning

Gotowa bibliografia na temat „Cognitive learning”

Autor: Grafiati

Data publikacji: 4 czerwca 2021

Data aktualizacji: 1 sierpnia 2024

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Artykuły w czasopismach na temat "Cognitive learning"

Pimple, Omkar, Umesh Saravane i Neha Gavankar. "Cognitive Learning Using Distributed Artificial Intelligence". International Journal of Machine Learning and Computing 5, nr 1 (luty 2015): 7–11. http://dx.doi.org/10.7763/ijmlc.2015.v5.474.

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G. B., Najimova, i Kartbaeva N. "Cognitive Linguistics In Language Learning Process". American Journal of Social Science and Education Innovations 02, nr 12 (31.12.2020): 407–12. http://dx.doi.org/10.37547/tajssei/volume02issue12-70.

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Cognitive linguistics is concerned with language in use, viewing language as a social phenomenon rather than simply a series of rules and structures. It is on this sense that this paper addresses the specific and essential roles of it in the English classroom from the perspective of cognitive linguistics. The article deals with the contribution of cognitive linguistics to the learning process with miming and body language.

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Buechel, Séverine D., Annika Boussard, Alexander Kotrschal, Wouter van der Bijl i Niclas Kolm. "Brain size affects performance in a reversal-learning test". Proceedings of the Royal Society B: Biological Sciences 285, nr 1871 (24.01.2018): 20172031. http://dx.doi.org/10.1098/rspb.2017.2031.

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It has become increasingly clear that a larger brain can confer cognitive benefits. Yet not all of the numerous aspects of cognition seem to be affected by brain size. Recent evidence suggests that some more basic forms of cognition, for instance colour vision, are not influenced by brain size. We therefore hypothesize that a larger brain is especially beneficial for distinct and gradually more complex aspects of cognition. To test this hypothesis, we assessed the performance of brain size selected female guppies ( Poecilia reticulata ) in two distinct aspects of cognition that differ in cognitive complexity. In a standard reversal-learning test we first investigated basic learning ability with a colour discrimination test, then reversed the reward contingency to specifically test for cognitive flexibility. We found that large-brained females outperformed small-brained females in the reversed-learning part of the test but not in the colour discrimination part of the test. Large-brained individuals are hence cognitively more flexible, which probably yields fitness benefits, as they may adapt more quickly to social and/or ecological cognitive challenges. Our results also suggest that a larger brain becomes especially advantageous with increasing cognitive complexity. These findings corroborate the significance of brain size for cognitive evolution.

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Cardona, 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.

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ABSTRACT: L’invecchiamento della popolazione è un dato demografico mondiale che assume carattere rilevante in molti Paesi del cosiddetto “primo mondo”, Il concetto di anzianità oggigiorno non può più basarsi su dati misurabili che stabiliscono quando un individuo, nell’arco della sua vita, entra nella fase della vecchiaia. Si tratta di un concetto molto più ampio e articolato che riguarda dimensioni socio-sanitarie, psico-affettive, cognitive e culturali. È necessario dunque ripensare il ruolo attivo della popolazione anziana in una società complessa e plurilingue. Nell’ottica dell’invecchiamento di successo (succesful ageing) e in base al principio di cittadinanza attiva (active citizenship) l’apprendimento delle lingue diviene un aspetto educativo rilevante sia per la partecipazione attiva nella società, sia per i vantaggi cognitivi specifici che tale tipo di apprendimento comporta. Oggi la ricerca neuropsicologica dimostra come l’apprendimento possa avvenire lungo tutto l’arco della vita e come il nostro cervello sia in grado di attivare importati fenomeni di compensazione in grado di arginare il declino cognitivo. In questo contributo si prenderanno in considerazione alcuni aspetti neuropsicologici che dimostrano come l’apprendimento linguistico nell’anziano non solo sia possibile, ma sia auspicabile. Su questi presupposti è importante che la linguistica educativa sviluppi un adeguato modello glotto-geragogico.Parole chiave: Glotto-geragogia. Anziani. Linguistica educativa. Plasticità neuronale. Riserva cognitiva. Modello STAC (Scaffolding Theory of Aging and Cognition). RESUMO: O envelhecimento da população é um dado demográfico global que assume um caráter relevante em muitos países do chamado "primeiro mundo". Hoje o conceito de antiguidade não pode mais ser baseado em dados mensuráveis que estabelecem quando um indivíduo, durante sua vida, entra na fase da velhice. É um conceito muito mais amplo e articulado que diz respeito às dimensões sócio-saúde, psicoafetiva, cognitiva e cultural. É, pois, necessário repensar o papel ativo da população idosa numa sociedade complexa e multilingue. Com vista a um envelhecimento bem sucedido e com base no princípio da cidadania ativa, a aprendizagem de línguas torna-se um aspecto educativo relevante tanto para a participação ativa na sociedade como para as vantagens cognitivas específicas que tal tipo de aprendizagem acarreta. Hoje, a pesquisa neuropsicológica demonstra como o aprendizado pode ocorrer ao longo da vida e como nosso cérebro é capaz de ativar importantes fenômenos de compensação capazes de conter o declínio cognitivo. Neste artigo, serão levados em consideração alguns aspectos neuropsicológicos que demonstram como a aprendizagem de linguagem em idosos não é apenas possível, mas desejável. Com base nesses pressupostos, é importante que a linguística educacional desenvolva um modelo gloto-hieragógico adequado.Palavras-chave: Gloto-hieragogia. Idosos. Linguística educacional. Plasticidade neuronal. Reserva cognitive. Modelo STAC (Scaffolding Theory of Aging and Cognition). ABSTRACT: Population aging is a world demographic data which assumes a relevant character in many of the countries of the so called “first world”. The concept of aging, nowadays, cannot be anymore based on measurable data that establish when a human being, throughout his life, enters the stage of old age. It deals with a much wider and more complex concept that concerns socio-health, psycho-affective, cognitive and cultural dimensions. It is therefore necessary to rethink the active role of old population in a complicated and multilingual society. With a view to a successful aging and according to the principle of active citizenship, language learning becomes an educational aspect relevant both in order to achieve an active social participation and for the specific cognitive advantages that type of learning provides with. Nowadays, the neuropsychological research shows how learning could happen throughout the entire life and how our brain is capable to activate important cognitive compensation phenomena capable of stemming the cognitive decline. This essay will take into consideration some neuropsychological aspects that demonstrate how language learning in old people is not only possible, but desirable. On these assumptions it is important that educational linguistic develops an adequate foreign language learning geragogic model. Keywords: Foreign language learning geragogic model. Old age. Educational linguistics. Neural plasticity. Brain reserve. STAC Model (Scaffolding Theory of Aging and Cognition).

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Jacobson, Wayne. "Learning, Culture, and Learning Culture". Adult Education Quarterly 47, nr 1 (listopad 1996): 15–28. http://dx.doi.org/10.1177/074171369604700102.

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Though adults have long faced the experience of learning to function in new cultural contexts, very little is understood about the processes of this sort of learning. This paper approaches learning culture from the position that cultural knowledge is best understood in terms of situated cognition. Contexts do not simply provide useful information in support of thinking and learning, but are inseparable from cognitive processes. Viewing culture in this way carries specific implications for understanding how a new culture is learned and how it might be taught. In particular, processes of learning culture can be seen to parallel processes of gaining practitioner knowledge, while processes of teaching culture can be modeled on the notion of cognitive apprenticeship.

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Shi, Zhongzhi. "Cognitive Machine Learning". International Journal of Intelligence Science 09, nr 04 (2019): 111–21. http://dx.doi.org/10.4236/ijis.2019.94007.

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Paradis, Rosemary D., Jinhong K. Guo, John Olden-Stahl i Jack Moulton. "Cognitive Category Learning". Procedia Computer Science 12 (2012): 188–93. http://dx.doi.org/10.1016/j.procs.2012.09.052.

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Chiriacescu, Vlad, Leen-Kiat Soh i Duane F. Shell. "Understanding Human Learning Using a Multi-agent Simulation of the Unified Learning Model". International Journal of Cognitive Informatics and Natural Intelligence 7, nr 4 (październik 2013): 1–25. http://dx.doi.org/10.4018/ijcini.2013100101.

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Within cognitive science and cognitive informatics, computational modeling based on cognitive architectures has been an important approach to addressing questions of human cognition and learning. This paper reports on a multi-agent computational model based on the principles of the Unified Learning Model (ULM). Derived from a synthesis of neuroscience, cognitive science, psychology, and education, the ULM merges a statistical learning mechanism with a general learning architecture. Description of the single agent model and the multi-agent environment which translate the principles of the ULM into an integrated computational model is provided. Validation results from simulations with respect to human learning are presented. Simulation suitability for cognitive learning investigations is discussed. Multi-agent system performance results are presented. Findings support the ULM theory by documenting a viable computational simulation of the core ULM components of long-term memory, motivation, and working memory and the processes taking place among them. Implications for research into human learning, cognitive informatics, intelligent agent, and cognitive computing are presented.

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Choi, Soonri, Soomin Kang, Kyungmin Lee, Hongjoo Ju i Jihoon Song. "The effect of an agent tutor’s integration of cognitive and emotional gestures on cognitive load, motivation, and achievement". Contemporary Educational Technology 16, nr 1 (5.01.2024): ep491. http://dx.doi.org/10.30935/cedtech/14101.

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This study proposes that the gestures of an agent tutor in a multimedia learning environment can generate positive and negative emotions in learners and influence their cognitive processes. To achieve this, we developed and integrated positive and negative agent tutor gestures in a multimedia learning environment directed by cognitive gestures. The effects of emotion type on cognition were examined in terms of cognitive load, learning motivation, and achievement. The subjects were 46 university students in Gyeonggi Province, South Korea. The students were divided into three learner groups: cognition, cognition + negative emotion, and cognition + positive emotion. The learners watched a tutorial lecture on the Notion note-taking app by an agent tutor. Data analysis was conducted using one-way ANOVA to determine the cognitive load, learning motivation, and achievement. The results showed that the positive emotion design was more effective in terms of intrinsic cognitive load, learning motivation, and achievement but had a higher extrinsic cognitive load. However, even the negative + passive group showed more positive learning than the cognition group. Although this study focused on gestures by an agent tutor, it implies that such gestures in multimedia learning contexts must be informed by emotional as well as cognitive design to provide a more meaningful learning experience.

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Kurniawati, Destriana, Ahmad Fauzi, Rini Budiharti i Fairusy F. Haryani. "Experimental Discovery Learning Model and TPACK-based Problem-Based Learning Supported Media Moodle Reviewed from Student Process Science Skills". Jurnal Penelitian Pendidikan IPA 9, nr 10 (25.10.2023): 8526–32. http://dx.doi.org/10.29303/jppipa.v9i10.5107.

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The research aims to find out whether or not there are: differences in the impact of the use of learning models discovery learning and problem-based learning supported by media Moodle on cognitive abilities; differences in the influence of students with high and low science process skills on cognitional capabilities; and interactions between learning models discovery learning and problem-based learning with the level of skills of science processes against cognitives on sound wave materials with the research subject being students of class XI SMAN 1 Surakarta teaching 2022–2023. The method used is the experimental method with a 2x2 factorial design. The data collection techniques used are the science process skills lift and the cognitive ability test of the learners. The data obtained was then analyzed using a two-way ANAVA test with cell frequencies not equal to the conclusion. It can be concluded that: there are differences in the impact of the use of discovery learning and problem-based learning supported by Moodle on cognitive abilities (Fobs = 18.606 > F0.05;1;67 = 3.98); there is a difference in the influence of learners who have high science process skills and low science process abilities on cognition abilities (Fobs = 15.65 > F0.05;1;67 = 3.98); and there is no interaction between the influences of learning models using discovery learning and problem-based learning in cognition (Fobs = 3.514 < F0.05;1;67 = 3.98).

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Rozprawy doktorskie na temat "Cognitive learning"

Fonooni, Benjamin. "Cognitive Interactive Robot Learning". Doctoral thesis, Umeå universitet, Institutionen för datavetenskap, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-97422.

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Building general purpose autonomous robots that suit a wide range of user-specified applications, requires a leap from today's task-specific machines to more flexible and general ones. To achieve this goal, one should move from traditional preprogrammed robots to learning robots that easily can acquire new skills. Learning from Demonstration (LfD) and Imitation Learning (IL), in which the robot learns by observing a human or robot tutor, are among the most popular learning techniques. Showing the robot how to perform a task is often more natural and intuitive than figuring out how to modify a complex control program. However, teaching robots new skills such that they can reproduce the acquired skills under any circumstances, on the right time and in an appropriate way, require good understanding of all challenges in the field. Studies of imitation learning in humans and animals show that several cognitive abilities are engaged to learn new skills correctly. The most remarkable ones are the ability to direct attention to important aspects of demonstrations, and adapting observed actions to the agents own body. Moreover, a clear understanding of the demonstrator's intentions and an ability to generalize to new situations are essential. Once learning is accomplished, various stimuli may trigger the cognitive system to execute new skills that have become part of the robot's repertoire. The goal of this thesis is to develop methods for learning from demonstration that mainly focus on understanding the tutor's intentions, and recognizing which elements of a demonstration need the robot's attention. An architecture containing required cognitive functions for learning and reproduction of high-level aspects of demonstrations is proposed. Several learning methods for directing the robot's attention and identifying relevant information are introduced. The architecture integrates motor actions with concepts, objects and environmental states to ensure correct reproduction of skills. Another major contribution of this thesis is methods to resolve ambiguities in demonstrations where the tutor's intentions are not clearly expressed and several demonstrations are required to infer intentions correctly. The provided solution is inspired by human memory models and priming mechanisms that give the robot clues that increase the probability of inferring intentions correctly. In addition to robot learning, the developed techniques are applied to a shared control system based on visual servoing guided behaviors and priming mechanisms. The architecture and learning methods are applied and evaluated in several real world scenarios that require clear understanding of intentions in the demonstrations. Finally, the developed learning methods are compared, and conditions where each of them has better applicability are discussed.
Att bygga autonoma robotar som passar ett stort antal olika användardefinierade applikationer kräver ett språng från dagens specialiserade maskiner till mer flexibla lösningar. För att nå detta mål, bör man övergå från traditionella förprogrammerade robotar till robotar som själva kan lära sig nya färdigheter. Learning from Demonstration (LfD) och Imitation Learning (IL), där roboten lär sig genom att observera en människa eller en annan robot, är bland de mest populära inlärningsteknikerna. Att visa roboten hur den ska utföra en uppgift är ofta mer naturligt och intuitivt än att modifiera ett komplicerat styrprogram. Men att lära robotar nya färdigheter så att de kan reproducera dem under nya yttre förhållanden, på rätt tid och på ett lämpligt sätt, kräver god förståelse för alla utmaningar inom området. Studier av LfD och IL hos människor och djur visar att flera kognitiva förmågor är inblandade för att lära sig nya färdigheter på rätt sätt. De mest anmärkningsvärda är förmågan att rikta uppmärksamheten på de relevanta aspekterna i en demonstration, och förmågan att anpassa observerade rörelser till robotens egen kropp. Dessutom är det viktigt att ha en klar förståelse av lärarens avsikter, och att ha förmågan att kunna generalisera dem till nya situationer. När en inlärningsfas är slutförd kan stimuli trigga det kognitiva systemet att utföra de nya färdigheter som blivit en del av robotens repertoar. Målet med denna avhandling är att utveckla metoder för LfD som huvudsakligen fokuserar på att förstå lärarens intentioner, och vilka delar av en demonstration som ska ha robotens uppmärksamhet. Den föreslagna arkitekturen innehåller de kognitiva funktioner som behövs för lärande och återgivning av högnivåaspekter av demonstrationer. Flera inlärningsmetoder för att rikta robotens uppmärksamhet och identifiera relevant information föreslås. Arkitekturen integrerar motorkommandon med begrepp, föremål och omgivningens tillstånd för att säkerställa korrekt återgivning av beteenden. Ett annat huvudresultat i denna avhandling rör metoder för att lösa tvetydigheter i demonstrationer, där lärarens intentioner inte är klart uttryckta och flera demonstrationer är nödvändiga för att kunna förutsäga intentioner på ett korrekt sätt. De utvecklade lösningarna är inspirerade av modeller av människors minne, och en primingmekanism används för att ge roboten ledtrådar som kan öka sannolikheten för att intentioner förutsägs på ett korrekt sätt. De utvecklade teknikerna har, i tillägg till robotinlärning, använts i ett halvautomatiskt system (shared control) baserat på visuellt guidade beteenden och primingmekanismer. Arkitekturen och inlärningsteknikerna tillämpas och utvärderas i flera verkliga scenarion som kräver en tydlig förståelse av mänskliga intentioner i demonstrationerna. Slutligen jämförs de utvecklade inlärningsmetoderna, och deras applicerbarhet under olika förhållanden diskuteras.
INTRO

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Yoder, Ryan J. "Learning Cognitive Feedback Specificity during Training and the Effect on Learning for Cognitive Tasks". Ohio University / OhioLINK, 2009. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1256155902.

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Yoder, Ryan J. "Learning cognitive feedback specificity during training and the effect of learning for cognitive tasks". Ohio : Ohio University, 2009. http://www.ohiolink.edu/etd/view.cgi?ohiou1256155902.

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任春華 i Chun-wa Yum. "Learning strategies and cognitive engagement". Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2008. http://hub.hku.hk/bib/B41717053.

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Yum, Chun-wa. "Learning strategies and cognitive engagement". Click to view the E-thesis via HKUTO, 2008. http://sunzi.lib.hku.hk/hkuto/record/B41717053.

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Reeder, Sarah. "Relationships in Aging, Cognitive Processes, and Contingency Learning". TopSCHOLAR®, 2006. http://digitalcommons.wku.edu/theses/259.

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This study investigated the influence of age, processing speed, working memory,and associative processes on the acquisition of contingency information. Young and older adults completed positive (+.65) and negative (-.65) contingency tasks that measured their ability to discover the relationship between a symptom (e.g., FEVER) and a fictional disease (e.g., OLYALGIA). Both d' scores, i.e., contingency learning, and contingency estimates, i.e., contingency judgment, were examined. Participants were also asked to complete cognitive tasks that measure the constructs of processing speed, working memory resources, associative memory, and associative learning. Structural equation modeling was used to examine the direct and indirect relationships between processing speed, working memory resources, associative memory, associative learning, and positive and negative contingency learning and judgment for young and older adult groups. Young adults outperformed older adults on the cognitive tasks and on contingency learning and judgment tasks. However, age differences were smaller for the positive contingency than for negative contingency. A comparison of the structural equation models for young and older adults showed no relationship between any cognitive construct and negative contingency learning. However, young adults' judgment for the negative contingency was directly influenced by associative learning, while their learning and judgment for the positive contingency was directly influenced by associative memory. For older adults, working memory executive function directly influenced their judgment for the negative contingency and their learning and judgment for the positive contingency. Processing speed had an indirect effect on older adults' contingency learning and judgment that was mediated by working memory executive functioning. The differences in the young adults' models as well as the difference between the young and older adults' models for positive and negative contingencies suggest that while associative processing is important, it may not account for all of the variation in contingency learning and judgment. The young adults' models for the negative contingency task indicates that higher level processes, such as inductive reasoning, maybe involved in negative contingency judgment because the associative learning task required some level of hypothesis testing. In contrast, positive contingency learning and judgment could rely primarily on more basic associative processes. The present findings therefore suggest that an overall model of contingency learning must include both associative processes and inductive reasoning processes. Older adults' general contingency performance was most directly related to their working memory executive functioning, suggesting that the decline in their working memory has the strongest effect on their ability to acquire and use information about contingencies. In fact, the age related decline in working memory seems to affect older adults' ability to acquire both positive and negative contingencies. The similarities across the older adult models for positive and negative contingencies indicate that the underlying deficit in older adults' working memory executive functioning that affects their overall contingency learning and judgment performance. This basic working memory executive functioning deficit for older adults also explains why their models for positive and negative contingency did not exhibit direct relationships between associative tasks and contingency learning as observed for the young adult models.

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Wong, Pik-ha. "Cognitive obstacles in learning the laws of indices". [Hong Kong : University of Hong Kong], 1994. http://sunzi.lib.hku.hk/hkuto/record.jsp?B13834216.

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Strehler, Anne. "The relationship between cognitive load, cognitive style and multimedia learning". Thesis, Pretoria : [s.n.], 2008. http://upetd.up.ac.za/thesis/available/etd-10282008-120644/.

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Leboe, Jason P. Milliken Bruce. "The inferential basis of perceptual performance /". *McMaster only, 2002.

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Eleftheriou, Georgie. "Cognitive reinforcement learning across the lifespan". Thesis, University of Surrey, 2013. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.616939.

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From the moment of birth, there is a direct interaction to our environment. This is the main principle of learning (Sutton & Barto, 1998). For instance when a new-born is playing there is a sensory and motor connectivity to the environment by allowing the cause and effect information, the outcome of an action and the achievement of a goal to be learned (Sutton & Barto, 1998). Moreover, learning provides lmowledge about us and our environment. Wliether we learn to ride a bicycle in adolescence or learn to drive a car in adulthood we are conscious of the several responds to our actions, which we want to influence what is happening in our behaviour during the lifespan. Learning from interactions is considered as an important field of research, underlying all theories of behaviour. The approached theory we explored is called reinforcement learning and it is more goal directed learning from interaction. Previous work in this area has focused mainly to what extend people learn to make choices that lead to positive outcomes and avoid making those that lead to negative outcomes (Frank et aI., 2004). This thesis extended this work by behaviourally investigating reinforcement learning across life span (7-55 years old). Using the reward bias coefficient (RBc) a measurement of an individual's tendency to follow reward or avoid punishment, in the face ofunceliainty, results demonstrated significant age differences in learning from positive and negative probablistic feedback between these age groups (7-10, 11-14, 15-18, 19-35 and 40-55 years old). This is the first study to compare qualitative changes in RBc across lifespan indicating the importance of positive and negative feedback learning in cognitive perfomance.

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Książki na temat "Cognitive learning"

Stephen, Rayner, red. Cognitive styles and learning strategies: Understanding style differences in learning and behaviour. London: D. Fulton Publishers, 1998.

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Speelman, Craig P. Beyond the learning curve. New York: Oxford University Press, 2005.

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Kommers, Piet A. M., David H. Jonassen, J. Terry Mayes i Alcindo Ferreira, red. Cognitive Tools for Learning. Berlin, Heidelberg: Springer Berlin Heidelberg, 1992. http://dx.doi.org/10.1007/978-3-642-77222-1.

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Eccles, John. Neurobiology of Cognitive Learning. Wiesbaden: VS Verlag für Sozialwissenschaften, 1992. http://dx.doi.org/10.1007/978-3-322-90053-1.

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Kommers, Piet A. M. Cognitive Tools for Learning. Berlin, Heidelberg: Springer Berlin Heidelberg, 1992.

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M, Kommers Piet A., Jonassen David H. 1947-, Mayes J. Terry, North Atlantic Treaty Organization. Scientific Affairs Division. i NATO Advanced Study Institute on Mindtools: Cognitive Technologies for Modeling Knowledge (1990 : Enschede, Netherlands), red. Cognitive tools for learning. Berlin: Springer-Verlag, 1992.

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Ross, Brian H., i Jose P. Mestre. The psychology of learning and motivation: Cognition in education. San Diego, Calif: Elsevier, 2011.

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J, Riding R., i Rayner Stephen, red. Cognitive styles. Stamford, Conn: Ablex Publishing Corp., 2000.

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Jin, Zheng. Exploring implicit cognition: Learning, memory, and social cognitive processes. Hershey, PA: Information Science Reference, an impring of IGI Global, 2015.

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

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Części książek na temat "Cognitive learning"

Mayer, Richard E. "Cognitive Learning". W Encyclopedia of the Sciences of Learning, 594–96. Boston, MA: Springer US, 2012. http://dx.doi.org/10.1007/978-1-4419-1428-6_390.

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Fürnkranz, Johannes, Dragan Gamberger i Nada Lavrač. "Beyond Concept Learning". W Cognitive Technologies, 217–46. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-540-75197-7_10.

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Fürnkranz, Johannes, Dragan Gamberger i Nada Lavrač. "Learning Single Rules". W Cognitive Technologies, 113–33. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-540-75197-7_6.

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Fürnkranz, Johannes, Dragan Gamberger i Nada Lavrač. "Learning Rule Sets". W Cognitive Technologies, 171–86. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-540-75197-7_8.

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Taylor, Sandie, i Lance Workman. "Memory and learning". W Cognitive Psychology, 84–126. London: Routledge, 2021. http://dx.doi.org/10.4324/9781003014355-4.

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Der, Ralf, i Georg Martius. "Model Learning". W Cognitive Systems Monographs, 183–200. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-20253-7_9.

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Leahey, Thomas Hardy. "Learning". W Fundamentals of Cognitive Science, 91–119. New York: Routledge, 2022. http://dx.doi.org/10.4324/9780429322822-4.

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Sense, Andrew. "Cognitive Style". W Cultivating Learning within Projects, 66–92. London: Palgrave Macmillan UK, 2007. http://dx.doi.org/10.1057/9780230591967_4.

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Grocott, Lisa. "Cognitive Psychology". W Design for Transformative Learning, 145–61. London: Routledge, 2022. http://dx.doi.org/10.4324/9780429429743-13.

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Hinkel, Eli. "Cognitive-Code Learning". W Encyclopedia of the Sciences of Learning, 625–26. Boston, MA: Springer US, 2012. http://dx.doi.org/10.1007/978-1-4419-1428-6_737.

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Streszczenia konferencji na temat "Cognitive learning"

Liu, Xiaotong, Anbang Xu, Zhe Liu, Yufan Guo i Rama Akkiraju. "Cognitive Learning". W CHI '19: CHI Conference on Human Factors in Computing Systems. New York, NY, USA: ACM, 2019. http://dx.doi.org/10.1145/3290607.3312844.

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Ruiz, Natalie, Qian Qian Feng, Ronnie Taib, Tara Handke i Fang Chen. "Cognitive skills learning". W International Conference on Multimodal Interfaces and the Workshop on Machine Learning for Multimodal Interaction. New York, New York, USA: ACM Press, 2010. http://dx.doi.org/10.1145/1891903.1891955.

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Jamalian, A. H., R. Rezvani i SH Mehrabi. "Learning in proximity". W Cognitive Computing (ICCI-CC). IEEE, 2011. http://dx.doi.org/10.1109/coginf.2011.6016127.

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Jamalian, Amirhossein, i Shamim Mehrabi. "Emotional Learning Automaton". W 2022 IEEE 21st International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC). IEEE, 2022. http://dx.doi.org/10.1109/iccicc57084.2022.10101665.

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Wong, B. L. William. "Learning Cognitive Task Analysis". W HCI Educators 2009 - playing with our education. BCS Learning & Development, 2009. http://dx.doi.org/10.14236/ewic/hcied2009.6.

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N R, Gladiss Merlin, Tamizh Malar S G, Prathiksha K, Nivedha P i Pavithra K. "Pictoquizezz: Engaging Cognitive Learning". W 2023 International Conference on Research Methodologies in Knowledge Management, Artificial Intelligence and Telecommunication Engineering (RMKMATE). IEEE, 2023. http://dx.doi.org/10.1109/rmkmate59243.2023.10369158.

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Chiriacescu, Vlad, Leen-Kiat Soh i Duane F. Shell. "Understanding human learning using a multi-agent simulation of the unified learning model". W 2013 12th IEEE International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC). IEEE, 2013. http://dx.doi.org/10.1109/icci-cc.2013.6622237.

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Takahata, Kei, i Takao Miura. "Reinforcement Learning using Kalman Filters". W 2019 IEEE 18th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC). IEEE, 2019. http://dx.doi.org/10.1109/iccicc46617.2019.9146066.

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Zhang, Du. "Learning through explaining observed inconsistencies". W 2014 IEEE 13th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC). IEEE, 2014. http://dx.doi.org/10.1109/icci-cc.2014.6921452.

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Zhang, Du. "Learning through overcoming temporal inconsistencies". W 2015 IEEE 14th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC). IEEE, 2015. http://dx.doi.org/10.1109/icci-cc.2015.7259378.

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Raporty organizacyjne na temat "Cognitive learning"

Liu, Mingyan. Cognitive Tactical Radios: Cognition Through Learning and Strategy (CLearStrategy). Fort Belvoir, VA: Defense Technical Information Center, grudzień 2012. http://dx.doi.org/10.21236/ada586790.

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Whitney, Paul. Learning from Text: A Cognitive Control Perspective. Fort Belvoir, VA: Defense Technical Information Center, maj 1992. http://dx.doi.org/10.21236/ada251842.

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Smith, Shelley. The cognitive learning styles of international students. Portland State University Library, styczeń 2000. http://dx.doi.org/10.15760/etd.5615.

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Jiang, Zhong-Ping. Cognitive Models for Learning to Control Dynamic Systems. Fort Belvoir, VA: Defense Technical Information Center, wrzesień 2008. http://dx.doi.org/10.21236/ada487160.

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Studak, Cathryn M., i Diana L. Allison. Developing Interdisciplinary Partnerships Based on Cognitive Learning Styles. Ames: Iowa State University, Digital Repository, listopad 2016. http://dx.doi.org/10.31274/itaa_proceedings-180814-1331.

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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.

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Streszczenie:

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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Ohlsson, Stellan. The Cognitive Function of Theoretical Knowledge in Procedural Learning. Fort Belvoir, VA: Defense Technical Information Center, marzec 1992. http://dx.doi.org/10.21236/ada248075.

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Reiser, Brian J., William A. Copen, Michael Ranney, Adnan Hamid i Daniel Y. Kimberg. Cognitive and Motivational Consequences of Tutoring and Discovery Learning. Fort Belvoir, VA: Defense Technical Information Center, czerwiec 1998. http://dx.doi.org/10.21236/ada347269.

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Gastelum, Zoe, Laura Matzen, Mallory Stites, Kristin Divis, Breannan Howell, Aaron Jones i Michael Trumbo. Assessing Cognitive Impacts of Errors from Machine Learning and Deep Learning Models: Final Report. Office of Scientific and Technical Information (OSTI), wrzesień 2021. http://dx.doi.org/10.2172/1821527.

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Gaines, David A. Assessing the Cognitive Abilities of Alternate Learning Classifier System Architectures. Fort Belvoir, VA: Defense Technical Information Center, lipiec 2003. http://dx.doi.org/10.21236/ada416405.

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