Relevant bibliographies by topics / Implicit learning

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Implicit learning'

Author: Grafiati
Published: 4 June 2021
Last updated: 25 July 2025

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Journal articles on the topic "Implicit learning"

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Holyoak, Keith J., and Merideth Gattis. "Implicit assumptions about implicit learning." Behavioral and Brain Sciences 17, no. 3 (1994): 406–7. http://dx.doi.org/10.1017/s0140525x00035159.

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Seger, Carol Augart. "Implicit learning." Psychological Bulletin 115, no. 2 (1994): 163–96. http://dx.doi.org/10.1037/0033-2909.115.2.163.

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Frensch, Peter A., and Dennis Rünger. "Implicit Learning." Current Directions in Psychological Science 12, no. 1 (2003): 13–18. http://dx.doi.org/10.1111/1467-8721.01213.

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Implicit learning appears to be a fundamental and ubiquitous process in cognition. Although defining and operationalizingimplicit learning remains a central theoretical challenge, scientists' understanding of implicit learning has progressed significantly. Beyond establishing the existence of “learning without awareness,” current research seeks to identify the cognitive processes that support implicit learning and addresses the relationship between learning and awareness of what was learned. The emerging view of implicit learning emphasizes the role of associative learning mechanisms that expl
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El Ghaoui, Laurent, Fangda Gu, Bertrand Travacca, Armin Askari, and Alicia Tsai. "Implicit Deep Learning." SIAM Journal on Mathematics of Data Science 3, no. 3 (2021): 930–58. http://dx.doi.org/10.1137/20m1358517.

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Liu, Yan, Yang Liu, Shenghua Zhong, and Songtao Wu. "Implicit Visual Learning." ACM Transactions on Intelligent Systems and Technology 8, no. 2 (2017): 1–24. http://dx.doi.org/10.1145/2974024.

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Hendrickx, Hilde, and Jan De Houwer. "Implicit Covariation Learning." Psychologica Belgica 37, no. 1-2 (1997): 29. http://dx.doi.org/10.5334/pb.907.

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Ennen, Elizabeth. "Implicit practical learning." Behavioral and Brain Sciences 17, no. 3 (1994): 404–5. http://dx.doi.org/10.1017/s0140525x00035135.

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Frick, Robert W., and Yuh-Shiow Lee. "Implicit Learning and Concept Learning." Quarterly Journal of Experimental Psychology Section A 48, no. 3 (1995): 762–82. http://dx.doi.org/10.1080/14640749508401414.

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In Experiments 1 and 2, subjects were exposed to letter strings that followed a pattern—the second letter was always the same. This exposure was disguised as a test of immediate memory. Following this training, subjects could discriminate new letter strings following the pattern from letter strings not following the pattern more often than would be expected by chance, which is the traditional evidence for concept learning. Discrimination was also better than would be predicted from subjects’ explicit report of the pattern, demonstrating the co-occurrence of concept learning and implicit learni
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Kuhn, Gustav, and Zoltán Dienes. "Implicit Learning of Nonlocal Musical Rules: Implicitly Learning More Than Chunks." Journal of Experimental Psychology: Learning, Memory, and Cognition 31, no. 6 (2005): 1417–32. http://dx.doi.org/10.1037/0278-7393.31.6.1417.

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Abrahamse, Elger. "Editorial to the special issue Implicit Serial Learning." Advances in Cognitive Psychology 8, no. 2 (2012): 70–72. http://dx.doi.org/10.5709/acp-0104-2.

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Dissertations / Theses on the topic "Implicit learning"

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Riedel, Beate. "Auditory implicit learning." Thesis, University of Glasgow, 2003. http://theses.gla.ac.uk/4884/.

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It has been suggested that much of the information we acquire from our external environment involves processes that do not require conscious awareness (e.g. Reber, 1989; Reber and Winter, 1994). Such knowledge acquisition has been termed implicit learning and this has been put forward as a fundamental process in allowing learning of complex information (e.g. Reber, 1992; Schmidke and Heuer, 1997). It has been proposed that acquisition of the underlying rule structure of stimulus events provides an indication of such a process as being fundamental and general. In contrast, learning bound to mor
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Kelly, Stephen William. "Experiments in implicit learning." Thesis, University of Glasgow, 1997. http://theses.gla.ac.uk/4989/.

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This thesis examines two paradigms from the area of implicit learning in detail. The literature suggests that the invariance detection paradigm of McGeorge and Burton (1990) gives rise to unconscious knowledge held at a conceptual level with the decision process served by a 'nearest-neighbour' similarity mechanism. The experiments in this thesis suggest that several aspects of this task do indeed seem to agree with present conceptions of unconscious knowledge but no evidence could be found that this knowledge is held at a conceptual level or that specific similarity plays any role in this task
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Bright, James. "Issues in implicit learning." Thesis, University of Nottingham, 1993. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.359867.

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Gebauer, Guido Felix. "Implicit learning and intelligence." Thesis, University of Cambridge, 2003. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.619521.

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Paciorek, Albertyna. "Implicit learning of semantic preferences." Thesis, University of Cambridge, 2013. https://www.repository.cam.ac.uk/handle/1810/244632.

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The research presented in this PhD dissertation examines the phenomenon of semantic implicit learning, using semantic preferences of novel verbs as a test case. Implicit learning refers to the phenomenon of learning without intending to learn or awareness that one is learning at all. Semantic preference (or selectional preference – as preferred in computational linguistics) is the tendency of a word to co-occur with words sharing similar semantic features. For example, ‘drink’ is typically followed by nouns denoting LIQUID, and the verb ‘chase’ is typically followed by ANIMATE nouns. The mater
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Huddy, Vyvyan. "Active processing in implicit learning." Thesis, University of Glasgow, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.390696.

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Wall, Matthew Bryan. "Emotional influences on implicit learning." Thesis, University of Cambridge, 2009. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.611312.

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Al-Hoorie, Ali. "Implicit attitudes in language learning." Thesis, University of Nottingham, 2017. http://eprints.nottingham.ac.uk/42571/.

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The field of language motivation is almost 60 years old. Throughout these decades, one idea has been persistent: Motivation is assumed to be a conscious process on which the learner can exert direct control. That this conscious conceptualization might not give the full picture has not been seriously entertained. An important consequence ensuing from this approach is the overreliance on self-report measures, such as questionnaires and interviews. Thus, in effect, the individual’s conscious reflection on their own attitudes and motivation has been the primary source of empirical data for our fie
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CAMPOLONGO, NICOLO'. "ADAPTIVE AND IMPLICIT ONLINE LEARNING." Doctoral thesis, Università degli Studi di Milano, 2021. http://hdl.handle.net/2434/823932.

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This thesis is dedicated to the study of online learning algorithms. In particular, after reviewing fundamental concepts in the theory of convex and online linear optimization, we provide a refined analysis of Implicit updates in the framework of Online Mirror Descent. We design a new adaptive algorithm based on it and carefully study its regret bound in the static case, linking it to the variability of the sequence of loss functions. Furthermore, we extend its application to the dynamic setting, studying its dynamic regret. In particular, we show that it achieves the optimal dynamic regret bo
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Rebuschat, Patrick. "Implicit learning of natural language syntax." Thesis, University of Cambridge, 2009. https://www.repository.cam.ac.uk/handle/1810/237038.

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The findings reported in this dissertation have several implications for our understanding of language acquisition and for future research. Firstly, while the precise form of the knowledge acquired in these experiments is unclear, the findings provided no evidence for rule learning in the vast majority of subjects. It suggests that subjects in these types of experiments (and perhaps in natural language acquisition) do not acquire linguistic rules. The results support Shanks (1995; Johnstone & Shanks, 2001), who argues against the possibility of implicit rule learning. Secondly, while adults ca
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Books on the topic "Implicit learning"

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name, No. Attention and implicit learning. John Benjamins Pub., 2003.

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Jiménez, Luis, ed. Attention and Implicit Learning. John Benjamins Publishing Company, 2003. http://dx.doi.org/10.1075/aicr.48.

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1964-, Jiménez Luis, ed. Attention and implicit learning. John Benjamins Publishing Company, Netherlands, 2002.

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A, Stadler Michael, and Frensch Peter A, eds. Handbook of implicit learning. Sage Publications, 1998.

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P, Dienes Zoltan, ed. Implicit learning: Theoretical and empirical issues. L. Erlbaum Associates, 1993.

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Dianne, Berry, ed. How implicit is implicit learning?: Edited by Dianne C. Berry. Oxford University Press, 1997.

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Rebuschat, Patrick, ed. Implicit and Explicit Learning of Languages. John Benjamins Publishing Company, 2015. http://dx.doi.org/10.1075/sibil.48.

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Rebuschat, Patrick. Implicit and explicit learning of languages. John Benjamins Publishing Company, 2015.

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C, Ellis Nick, ed. Implicit and explicit learning of languages. Academic Press, 1994.

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Axel, Cleeremans, ed. Implicit learning and consciousness: An empirical, philosophical, and computational consensus in the making. Psychology Press, 2002.

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Book chapters on the topic "Implicit learning"

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Yi, Hu. "Implicit Learning." In The ECPH Encyclopedia of Psychology. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-99-6000-2_1091-1.

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Yi, Hu. "Implicit Learning." In The ECPH Encyclopedia of Psychology. Springer Nature Singapore, 2024. https://doi.org/10.1007/978-981-97-7874-4_1091.

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Meier, Beat, and Josephine Cock. "Implicit Sequence Learning." In Encyclopedia of the Sciences of Learning. Springer US, 2012. http://dx.doi.org/10.1007/978-1-4419-1428-6_515.

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Paciorek, Albertyna, and John N. Williams. "Semantic implicit learning." In Implicit and Explicit Learning of Languages. John Benjamins Publishing Company, 2015. http://dx.doi.org/10.1075/sibil.48.04pac.

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Mulligan, Neil W., and Alan S. Brown. "Attention and implicit memory." In Attention and Implicit Learning. John Benjamins Publishing Company, 2003. http://dx.doi.org/10.1075/aicr.48.16mul.

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Jiménez, Luis. "Introduction." In Attention and Implicit Learning. John Benjamins Publishing Company, 2003. http://dx.doi.org/10.1075/aicr.48.03jim.

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Shanks, David R. "Attention and awareness in “implicit” sequence learning." In Attention and Implicit Learning. John Benjamins Publishing Company, 2003. http://dx.doi.org/10.1075/aicr.48.05sha.

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Jiménez, Luis. "Intention, attention, and consciousness in probabilistic sequence learning." In Attention and Implicit Learning. John Benjamins Publishing Company, 2003. http://dx.doi.org/10.1075/aicr.48.06jim.

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Hazeltine, Eliot, and Richard B. Ivry. "Neural structures that support implicit sequence learning." In Attention and Implicit Learning. John Benjamins Publishing Company, 2003. http://dx.doi.org/10.1075/aicr.48.08haz.

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Ashby, F. Gregory, and Michael B. Casale. "The cognitive neuroscience of implicit category learning." In Attention and Implicit Learning. John Benjamins Publishing Company, 2003. http://dx.doi.org/10.1075/aicr.48.09ash.

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Conference papers on the topic "Implicit learning"

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Liu, Heping, Songbai Liu, Junkai Ji, Qiuzhen Lin, Jianyong Chen, and Kay Chen Tan. "Personalized Federated Learning with Enhanced Implicit Generalization." In 2024 International Joint Conference on Neural Networks (IJCNN). IEEE, 2024. http://dx.doi.org/10.1109/ijcnn60899.2024.10651164.

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Hong, Junyoung, Hyeri Yang, Ye Ju Kim, et al. "D2FP: Learning Implicit Prior for Human Parsing." In 2025 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV). IEEE, 2025. https://doi.org/10.1109/wacv61041.2025.00883.

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Fan, Shen, and Przemyslaw Musialski. "Optimizing 3D Geometry Reconstruction from Implicit Neural Representations." In 2024 International Conference on Machine Learning and Applications (ICMLA). IEEE, 2024. https://doi.org/10.1109/icmla61862.2024.00117.

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Zhou, Xiaoling, Wei Ye, Yidong Wang, et al. "Enhancing In-Context Learning via Implicit Demonstration Augmentation." In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). Association for Computational Linguistics, 2024. http://dx.doi.org/10.18653/v1/2024.acl-long.155.

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Han, Xinchen, Hossam Afifi, and Michel Marot. "Cosine Similarity Based Adaptive Implicit Q-Learning for Offline Reinforcement Learning." In 2025 IEEE Wireless Communications and Networking Conference (WCNC). IEEE, 2025. https://doi.org/10.1109/wcnc61545.2025.10978817.

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Weitl-Harms, Sherri, John D. Hastings, and Jonah Lum. "Using LLMs to Establish Implicit User Sentiment of Software Desirability." In 2024 International Conference on Machine Learning and Applications (ICMLA). IEEE, 2024. https://doi.org/10.1109/icmla61862.2024.00254.

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Nute, Addison, Matthew Judah, and Scott Kuzdeba. "Radio frequency signal generation using denoising diffusion implicit models." In Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications VII, edited by Peter J. Schwartz and Myron E. Hohil. SPIE, 2025. https://doi.org/10.1117/12.3053823.

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Ravari, Amirhossein, Seyede Fatemeh Ghoreishi, and Mahdi Imani. "Implicit Human Perception Learning in Complex and Unknown Environments." In 2024 American Control Conference (ACC). IEEE, 2024. http://dx.doi.org/10.23919/acc60939.2024.10644975.

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Rong, Yuetong, and Yijun Mo. "NCPrompt: NSP-Based Prompt Learning and Contrastive Learning for Implicit Discourse Relation Recognition." In Findings of the Association for Computational Linguistics: EMNLP 2024. Association for Computational Linguistics, 2024. http://dx.doi.org/10.18653/v1/2024.findings-emnlp.63.

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Oechsle, Michael, Michael Niemeyer, Christian Reiser, Lars Mescheder, Thilo Strauss, and Andreas Geiger. "Learning Implicit Surface Light Fields." In 2020 International Conference on 3D Vision (3DV). IEEE, 2020. http://dx.doi.org/10.1109/3dv50981.2020.00055.

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Reports on the topic "Implicit learning"

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Bawaskar, Neerja. Analog Implicit Functional Testing using Supervised Machine Learning. Portland State University Library, 2000. http://dx.doi.org/10.15760/etd.2097.

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Jiang, Yuhong V. Implicit Learning of Complex Visual Contexts Under Non-Optimal Conditions. Defense Technical Information Center, 2007. http://dx.doi.org/10.21236/ada482119.

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Rupe, Adam. Learning Implicit Models of Complex Dynamical Systems From Partial Observations. Office of Scientific and Technical Information (OSTI), 2021. http://dx.doi.org/10.2172/1808822.

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Sun, Ron, and Robert C. Mathews. Exploring the Interaction of Implicit and Explicit Processes to Facilitate Individual Skill Learning. Defense Technical Information Center, 2005. http://dx.doi.org/10.21236/ada435130.

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Yu, Wanchi. Implicit Learning of Children with and without Developmental Language Disorder across Auditory and Visual Categories. Portland State University Library, 2000. http://dx.doi.org/10.15760/etd.7460.

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Matsekh, Anna M., Luis Chacon, HyeongKae Park, and Guangye Chen. Machine Learning for Memory Reduction in the Implicit Monte Carlo Simulations of Thermal Radiative Transfer. Office of Scientific and Technical Information (OSTI), 2020. http://dx.doi.org/10.2172/1618308.

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Lyuckanova, Miglena, Lora Lyamova, Yordanka Lalova, Antoaneta Kalonkina, Vasil Kolev, and Juliana Yordanova. The Role of Implicit Sequence Learning in the Auditory Modality for Phonological Awareness in Children. "Prof. Marin Drinov" Publishing House of Bulgarian Academy of Sciences, 2018. http://dx.doi.org/10.7546/crabs.2018.09.07.

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Zeidenstein, Sondra, and Kirsten Moore. Learning About Sexuality: A Practical Beginning. Population Council, 1996. http://dx.doi.org/10.31899/pgy1996.1007.

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“Learning About Sexuality: A Practical Beginning” is divided into three main parts. The first includes approaches that program staff, activists, and researchers are taking to understand people’s experiences of sexuality. The second explores the explicit and implicit links among health-seeking behavior, contraceptive practice, reproductive health, and sexuality. The chapters in part three focus on activities that challenge entrenched attitudes and behavior about sexuality that have real and potentially harmful effects on women’s and men’s reproductive health. The book features program and resea
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McKnight, Katherine, Nitya Venkateswaran, Jennifer Laird, Rita Dilig, Jessica Robles, and Talia Shalev. Parent Teacher Home Visits: An Approach to Addressing Biased Mindsets and Practices to Support Student Success. RTI Press, 2022. http://dx.doi.org/10.3768/rtipress.2022.op.0077.2209.

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Research has shown educators’ implicit biases to be a key factor in creating and perpetuating disparities in students’ experiences of schooling, learning, and longer-term outcomes, including job opportunities, wealth, and health. Current school reform and transformation efforts are aimed at addressing institutionalized racism in school policies, practices, and cultural systems by implementing implicit bias training for teachers and staff. In this paper, we explain how a school home visits program, Parent Teacher Home Visits (PTHV), is a promising intervention for counteracting implicit biases
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Baquero-Rey, Luis E., and Juan C. Ayala. Exploración y análisis de datos de texto masivos relativos a la crisis del COVID-19. Escuela Tecnológica Instituto Técnico Central, 2023. http://dx.doi.org/10.55411/2023.10.

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La pandemia del COVID-19 trajo consigo grandes implicaciones para la humanidad, desde poner el mundo en jaque frente a las restricciones de movilidad, hasta llegar a implicar nuevos retos para los científicos; con el fin de tratar y hacerle frente a la situación. Esto conllevó, entre otras cosas, al desarrollo de nuevas vacunas en tiempo récord, apoyados desde luego, también en áreas de las ciencias de la computación, como lo es la Inteligencia Artificial. El aprendizaje automático o machine learning es una rama de la inteligencia artificial que permite que las máquinas aprendan sin ser expres
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