Thematische Bibliographien / Visual learning

Inhaltsverzeichnis

  1. Zeitschriftenartikel
  2. Dissertationen
  3. Bücher
  4. Buchteile
  5. Konferenzberichte
  6. Berichte der Organisationen

Auswahl der wissenschaftlichen Literatur zum Thema „Visual learning“

Autor: Grafiati
Veröffentlicht am 4. Juni 2021
Zuletzt aktualisiert am 25. Juli 2025

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Zeitschriftenartikel zum Thema "Visual learning"

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Sze, Daniel Y. "Visual Learning." Journal of Vascular and Interventional Radiology 32, no. 3 (2021): 331. http://dx.doi.org/10.1016/j.jvir.2021.01.265.

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Nida, Diini Fitrahtun, Muhyiatul Fadilah, Ardi Ardi, and Suci Fajrina. "CHARACTERISTICS OF VISUAL LITERACY-BASED BIOLOGY LEARNING MODULE VALIDITY ON PHOTOSYNTHESIS LEARNING MATERIALS." JURNAL PAJAR (Pendidikan dan Pengajaran) 7, no. 4 (2023): 785. http://dx.doi.org/10.33578/pjr.v7i4.9575.

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Visual literacy is the skill to interpret and give meaning to information in the form of images or visuals. Visual literacy is included in the list of 21st-century skills. The observation results indicate that most of the students have not mastered visual literacy well. One of the efforts that can be made to improve visual literacy is the provision of appropriate and right teaching materials. The research is an R&D (Research and Development) using a 4-D model, which is modified to 3-D (define, design, develop). The instruments used were content analysis sheets and validation questionnaires
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A Sowe, Ebou. "Momentum Contrast for Unsupervised Visual Representation Learning." Journal of Advances in Civil and Mechanical Engineering 2, no. 1 (2025): 01–06. https://doi.org/10.64030/3067-2457.02.01.02.

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This brief report presents a novel unsupervised learning representation learning method called momentum contrast. Momentum contrast uses a contrastive learning technique to learn representations by comparing features of related yet dissimilar images for efficient feature extraction and unsupervised representation learning. Similar images are grouped together, and dissimilar images are placed far apart. The method builds upon previous works in contrastive learning but includes a momentum optimisation step to improve representation learning performance and generate better quality representations
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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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Cruz, Rodrigo Santa, Basura Fernando, Anoop Cherian, and Stephen Gould. "Visual Permutation Learning." IEEE Transactions on Pattern Analysis and Machine Intelligence 41, no. 12 (2019): 3100–3114. http://dx.doi.org/10.1109/tpami.2018.2873701.

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Jones, Rachel. "Visual learning visualized." Nature Reviews Neuroscience 4, no. 1 (2003): 10. http://dx.doi.org/10.1038/nrn1014.

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Lu, Zhong-Lin, Tianmiao Hua, Chang-Bing Huang, Yifeng Zhou, and Barbara Anne Dosher. "Visual perceptual learning." Neurobiology of Learning and Memory 95, no. 2 (2011): 145–51. http://dx.doi.org/10.1016/j.nlm.2010.09.010.

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Richler, Jennifer J., and Thomas J. Palmeri. "Visual category learning." Wiley Interdisciplinary Reviews: Cognitive Science 5, no. 1 (2013): 75–94. http://dx.doi.org/10.1002/wcs.1268.

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Guinibert, Matthew. "Learn from your environment: A visual literacy learning model." Australasian Journal of Educational Technology 36, no. 4 (2020): 173–88. http://dx.doi.org/10.14742/ajet.5200.

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Based on the presupposition that visual literacy skills are not usually learned unaided by osmosis, but require targeted learning support, this article explores how everyday encounters with visuals can be leveraged as contingent learning opportunities. The author proposes that a learner’s environment can become a visual learning space if appropriate learning support is provided. This learning support may be delivered via the anytime and anywhere capabilities of mobile learning (m-learning), which facilitates peer learning in informal settings. The study propositioned a rhizomatic m-learning mo
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Taga, Tadashi, Kazuhito Yoshizaki, and Kimiko Kato. "Visual field difference in visual statistical learning." Proceedings of the Annual Convention of the Japanese Psychological Association 79 (September 22, 2015): 2EV—074–2EV—074. http://dx.doi.org/10.4992/pacjpa.79.0_2ev-074.

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Dissertationen zum Thema "Visual learning"

1

Zhu, Fan. "Visual feature learning." Thesis, University of Sheffield, 2015. http://etheses.whiterose.ac.uk/8218/.

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Categorization is a fundamental problem of many computer vision applications, e.g., image classification, pedestrian detection and face recognition. The robustness of a categorization system heavily relies on the quality of features, by which data are represented. The prior arts of feature extraction can be concluded in different levels, which, in a bottom up order, are low level features (e.g., pixels and gradients) and middle/high-level features (e.g., the BoW model and sparse coding). Low level features can be directly extracted from images or videos, while middle/high-level features are co
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Goh, Hanlin. "Learning deep visual representations." Paris 6, 2013. http://www.theses.fr/2013PA066356.

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Les avancées récentes en apprentissage profond et en traitement d'image présentent l'opportunité d'unifier ces deux champs de recherche complémentaires pour une meilleure résolution du problème de classification d'images dans des catégories sémantiques. L'apprentissage profond apporte au traitement d'image le pouvoir de représentation nécessaire à l'amélioration des performances des méthodes de classification d'images. Cette thèse propose de nouvelles méthodes d'apprentissage de représentations visuelles profondes pour la résolution de cette tache. L'apprentissage profond a été abordé sous deu
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Walker, Catherine Livesay. "Visual learning through Hypermedia." CSUSB ScholarWorks, 1996. https://scholarworks.lib.csusb.edu/etd-project/1148.

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Owens, Andrew (Andrew Hale). "Learning visual models from paired audio-visual examples." Thesis, Massachusetts Institute of Technology, 2016. http://hdl.handle.net/1721.1/107352.

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Thesis: Ph. D., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2016.<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (pages 93-104).<br>From the clink of a mug placed onto a saucer to the bustle of a busy café, our days are filled with visual experiences that are accompanied by distinctive sounds. In this thesis, we show that these sounds can provide a rich training signal for learning visual models. First, we propose the task of predicting the sound that an object makes when struck as a way of studying physical
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Peyre, Julia. "Learning to detect visual relations." Thesis, Paris Sciences et Lettres (ComUE), 2019. http://www.theses.fr/2019PSLEE016.

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Nous étudions le problème de détection de relations visuelles de la forme (sujet, prédicat, objet) dans les images, qui sont des entités intermédiaires entre les objets et les scènes visuelles complexes. Cette thèse s’attaque à deux défis majeurs : (1) le problème d’annotations coûteuses pour l’entrainement de modèles fortement supervisés, (2) la variation d’apparence visuelle des relations. Nous proposons un premier modèle de détection de relations visuelles faiblement supervisé, n’utilisant que des annotations au niveau de l’image, qui, étant donné des détecteurs d’objets pré-entrainés, atte
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Wang, Zhaoqing. "Self-supervised Visual Representation Learning." Thesis, The University of Sydney, 2022. https://hdl.handle.net/2123/29595.

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In general, large-scale annotated data are essential to training deep neural networks in order to achieve better performance in visual feature learning for various computer vision applications. Unfortunately, the amount of annotations is challenging to obtain, requiring a high cost of money and human resources. The dependence on large-scale annotated data has become a crucial bottleneck in developing an advanced intelligence perception system. Self-supervised visual representation learning, a subset of unsupervised learning, has gained popularity because of its ability to avoid the high cost
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Tang-Wright, Kimmy. "Visual topography and perceptual learning in the primate visual system." Thesis, University of Oxford, 2016. https://ora.ox.ac.uk/objects/uuid:388b9658-dceb-443a-a19b-c960af162819.

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The primate visual system is organised and wired in a topological manner. From the eye well into extrastriate visual cortex, a preserved spatial representation of the vi- sual world is maintained across many levels of processing. Diffusion-weighted imaging (DWI), together with probabilistic tractography, is a non-invasive technique for map- ping connectivity within the brain. In this thesis I probed the sensitivity and accuracy of DWI and probabilistic tractography by quantifying its capacity to detect topolog- ical connectivity in the post mortem macaque brain, between the lateral geniculate
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Shi, Xiaojin. "Visual learning from small training datasets /." Diss., Digital Dissertations Database. Restricted to UC campuses, 2005. http://uclibs.org/PID/11984.

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Liu, Jingen. "Learning Semantic Features for Visual Recognition." Doctoral diss., University of Central Florida, 2009. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/3358.

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Visual recognition (e.g., object, scene and action recognition) is an active area of research in computer vision due to its increasing number of real-world applications such as video (image) indexing and search, intelligent surveillance, human-machine interaction, robot navigation, etc. Effective modeling of the objects, scenes and actions is critical for visual recognition. Recently, bag of visual words (BoVW) representation, in which the image patches or video cuboids are quantized into visual words (i.e., mid-level features) based on their appearance similarity using clustering, has been wi
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Beale, Dan. "Autonomous visual learning for robotic systems." Thesis, University of Bath, 2012. https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.558886.

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This thesis investigates the problem of visual learning using a robotic platform. Given a set of objects the robots task is to autonomously manipulate, observe, and learn. This allows the robot to recognise objects in a novel scene and pose, or separate them into distinct visual categories. The main focus of the work is in autonomously acquiring object models using robotic manipulation. Autonomous learning is important for robotic systems. In the context of vision, it allows a robot to adapt to new and uncertain environments, updating its internal model of the world. It also reduces the amount
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Bücher zum Thema "Visual learning"

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Katsushi, Ikeuchi, and Veloso Manuela M, eds. Symbolic visual learning. Oxford University Press, 1997.

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K, Nayar Shree, and Poggio Tomaso, eds. Early visual learning. Oxford University Press, 1996.

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M, Moore David, and Dwyer Francis M, eds. Visual literacy: A spectrum of visual learning. Educational Technology Publications, 1994.

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N, Erin Jane, ed. Visual handicaps and learning. 3rd ed. PRO-ED, 1992.

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Liberty, Jesse. Learning Visual Basic .NET. O'Reilly, 2002.

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Rourke, Adrianne. Improving visual teaching materials. Nova Science Publishers, 2009.

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Baratta, Alex. Visual writing. Cambridge Scholars, 2010.

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Manfred, Fahle, and Poggio Tomaso, eds. Perceptual learning. MIT Press, 2002.

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Vakanski, Aleksandar, and Farrokh Janabi-Sharifi. Robot Learning by Visual Observation. John Wiley & Sons, Inc., 2017. http://dx.doi.org/10.1002/9781119091882.

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Beatty, Grace Joely. PowerPoint: The visual learning guide. Prima Pub., 1994.

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Buchteile zum Thema "Visual learning"

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Burge, M., and W. Burger. "Learning visual ideals." In Image Analysis and Processing. Springer Berlin Heidelberg, 1997. http://dx.doi.org/10.1007/3-540-63508-4_138.

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Burge, M., and W. Burger. "Learning visual ideals." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 1997. http://dx.doi.org/10.1007/bfb0025067.

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Panciroli, Chiara, Laura Corazza, and Anita Macauda. "Visual-Graphic Learning." In Proceedings of the 2nd International and Interdisciplinary Conference on Image and Imagination. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-41018-6_6.

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Lu, Zhong-Lin, and Barbara Anne Dosher. "Visual Perceptual Learning." In Encyclopedia of the Sciences of Learning. Springer US, 2012. http://dx.doi.org/10.1007/978-1-4419-1428-6_258.

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Lovegrove, William. "The Visual Deficit Hypothesis." In Learning Disabilities. Springer New York, 1992. http://dx.doi.org/10.1007/978-1-4613-9133-3_8.

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Golon, Alexandra Shires. "Learning Styles Differentiation." In VISUAL-SPATIAL learners, 2nd ed. Routledge, 2021. http://dx.doi.org/10.4324/9781003239482-1.

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Golon, Alexandra Shires. "Learning Styles Differentiation." In VISUAL-SPATIAL learners, 2nd ed. Routledge, 2021. http://dx.doi.org/10.4324/9781003239482-1.

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Wu, Qi, Peng Wang, Xin Wang, Xiaodong He, and Wenwu Zhu. "Video Representation Learning." In Visual Question Answering. Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-0964-1_7.

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Wu, Qi, Peng Wang, Xin Wang, Xiaodong He, and Wenwu Zhu. "Deep Learning Basics." In Visual Question Answering. Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-0964-1_2.

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Grobstein, Paul, and Kao Liang Chow. "Visual System Development, Plasticity." In Learning and Memory. Birkhäuser Boston, 1989. http://dx.doi.org/10.1007/978-1-4899-6778-7_22.

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Konferenzberichte zum Thema "Visual learning"

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Liang, Anthony, Jesse Thomason, and Erdem Bıyık. "ViSaRL: Visual Reinforcement Learning Guided by Human Saliency." In 2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2024. https://doi.org/10.1109/iros58592.2024.10801388.

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Tsiamas, Ioannis, Santiago Pascual, Chunghsin Yeh, and Joan Serrà. "Sequential Contrastive Audio-Visual Learning." In ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2025. https://doi.org/10.1109/icassp49660.2025.10888656.

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Neuvonen, Heidi. "VISUAL LEARNING OF HIGHER EDUCATION E-LEARNING STUDENTS." In 17th annual International Conference of Education, Research and Innovation. IATED, 2024. https://doi.org/10.21125/iceri.2024.0513.

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de Luna, Robert G., Desiree M. Mendoza, Michaella R. Isada, et al. "VIScial: Visual Classification of Facial Shape Using Deep Transfer Learning." In 2024 IEEE International Conference on Imaging Systems and Techniques (IST). IEEE, 2024. https://doi.org/10.1109/ist63414.2024.10759179.

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Song, Yingjin, Denis Paperno, and Albert Gatt. "Context-aware Visual Storytelling with Visual Prefix Tuning and Contrastive Learning." In Proceedings of the 17th International Natural Language Generation Conference. Association for Computational Linguistics, 2024. https://doi.org/10.18653/v1/2024.inlg-main.32.

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Buijs, Jean M., and Michael S. Lew. "Learning visual concepts." In the seventh ACM international conference. ACM Press, 1999. http://dx.doi.org/10.1145/319878.319880.

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Zhao, Qi, and Christof Koch. "Learning visual saliency." In 2011 45th Annual Conference on Information Sciences and Systems (CISS). IEEE, 2011. http://dx.doi.org/10.1109/ciss.2011.5766178.

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BERARDI, NICOLETTA, and ADRIANA FIORENTINI. "VISUAL PERCEPTUAL LEARNING." In Proceedings of the International School of Biophysics. WORLD SCIENTIFIC, 2001. http://dx.doi.org/10.1142/9789812799975_0034.

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Ji, Daomin, Hui Luo, and Zhifeng Bao. "Visualization Recommendation Through Visual Relation Learning and Visual Preference Learning." In 2023 IEEE 39th International Conference on Data Engineering (ICDE). IEEE, 2023. http://dx.doi.org/10.1109/icde55515.2023.00145.

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Guangming Chang, Chunfen Yuan, and Weiming Hu. "Interclass visual similarity based visual vocabulary learning." In 2011 First Asian Conference on Pattern Recognition (ACPR 2011). IEEE, 2011. http://dx.doi.org/10.1109/acpr.2011.6166597.

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Berichte der Organisationen zum Thema "Visual learning"

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Bhanu, Bir. Learning Integrated Visual Database for Image Exploitation. Defense Technical Information Center, 2002. http://dx.doi.org/10.21236/ada413389.

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Edelman, Shimon, Heinrich H. Buelthoff, and Erik Sklar. Task and Object Learning in Visual Recognition. Defense Technical Information Center, 1991. http://dx.doi.org/10.21236/ada259961.

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Petrie, Christopher, and Katija Aladin. Spotlight: Visual Arts. HundrED, 2020. http://dx.doi.org/10.58261/azgu5536.

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HundrED and Supercell believe that fostering Visual Art skills can be just as important as numeracy and literacy. Furthermore, we also believe that Visual Arts can be integrated into all learning in schools and developed in a diversity of ways. To this end, the purpose of this project is to shine a spotlight, and make globally visible, leading education innovations from around the world doing exceptional work on developing the skill of Visual Arts for all students, teachers, and leaders in schools today.
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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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Poggio, Tomaso, and Stephen Smale. Hierarchical Kernel Machines: The Mathematics of Learning Inspired by Visual Cortex. Defense Technical Information Center, 2013. http://dx.doi.org/10.21236/ada580529.

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Harmon, Dr Jennifer. Exploring the Efficacy of Active and Authentic Learning in the Visual Merchandising Classroom. Iowa State University, Digital Repository, 2016. http://dx.doi.org/10.31274/itaa_proceedings-180814-1524.

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Mills, Kathy, Elizabeth Heck, Alinta Brown, Patricia Funnell, and Lesley Friend. Senses together : Multimodal literacy learning in primary education : Final project report. Institute for Learning Sciences and Teacher Education, Australian Catholic University, 2023. http://dx.doi.org/10.24268/acu.8zy8y.

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[Executive summary] Literacy studies have traditionally focussed on the seen. The other senses are typically under-recognised in literacy studies and research, where the visual sense has been previously prioritised. However, spoken and written language, images, gestures, touch, movement, and sound are part of everyday literacy practices. Communication is no longer focussed on visual texts but is a multisensory experience. Effective communication depends then on sensory orchestration, which unifies the body and its senses. Understanding sensory orchestration is crucial to literacy learning in t
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Nahorniak, Maya. Occupation of profession: Methodology of laboratory classes from practically-oriented courses under distance learning (on an example of discipline «Radioproduction»). Ivan Franko National University of Lviv, 2022. http://dx.doi.org/10.30970/vjo.2022.51.11412.

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The article deals with the peculiarities of the use of verbal, visual and practical methods in the distance learning of professional practically-oriented discipline «Radioproduction», are offered new techniques for the use of these methods during the presentation of theoretical material and the creation of a media product (audiovisual content), due to the acquisition of a specialty in conditions online. It is proved that in distance learning, this discipline is inadmissible to absolutize the significance of verbal methods (narrative, explanation, conversation, discussion, lecture) and that all
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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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Shepiliev, Dmytro S., Yevhenii O. Modlo, Yuliia V. Yechkalo, et al. WebAR development tools: An overview. CEUR Workshop Proceedings, 2021. http://dx.doi.org/10.31812/123456789/4356.

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Web augmented reality (WebAR) development tools aimed at improving the visual aspects of learning are far from being visual and available themselves. This causing problems of selecting and testing WebAR development tools for CS undergraduatesmastering inweb-design basics. The research is aimed at conducting comparative analysis of WebAR tools to select those appropriated for beginners.
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