Готові списки джерел за темами / In-Context learning

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Добірка наукової літератури з теми "In-Context learning"

Автор: Grafiati
Опубліковано: 30 листопада 2024

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Статті в журналах з теми "In-Context learning"

1

Paramita, Kristanti Dewi, and Yandi Andri Yatmo. "CONTEXT LEARNING TRANSFORMATION IN ARCHITECTURAL DESIGN STUDIO." MODUL 20, no. 2 (December 15, 2020): 157–66. http://dx.doi.org/10.14710/mdl.20.2.2020.157-166.

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This paper reflects the shifting understanding of context in a data-based architectural studio. With the school closure in the beginning of the COVID pandemic, the overall learning process is largely conducted online. Big Data becomes an important discourse that provides some benefits and opportunities which transform the design and learning process in an architectural studio, particularly on how students may explore and understand their context. Exploring the works of third-year architectural students in Department of Architecture, Faculty of Engineering Universitas Indonesia, this paper highlights the ways students capture and organise urban information and construct their intervention contexts. The study points out that time, flow and narrative are key in transforming understanding of context. Based on such three aspects, the data reveals the unseen urban patterns, emerging in the imbalance relationship between user and the environment, the disconnection of urban services, and the hidden variety of urban experience. The study reflects how these urban patterns informs the ways students define and situate themselves in the context, shifting existing ideas of context and its corresponding methodologies in the architectural education.
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2

DiSalvo, Betsy James, Kevin Crowley, and Roy Norwood. "Learning in Context." Games and Culture 3, no. 2 (April 2008): 131–41. http://dx.doi.org/10.1177/1555412008314130.

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3

Li, Chieh, and Ena Vazquez Nuttall. "Context, Context, Context: Powerful Factor in Language Learning." Contemporary Psychology: A Journal of Reviews 41, no. 5 (May 1996): 501–3. http://dx.doi.org/10.1037/004472.

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4

Keiding, Tina Bering. "Learning in context – But what is a learning context?" Nordic Studies in Education 27, no. 02 (May 29, 2007): 138–50. http://dx.doi.org/10.18261/issn1891-5949-2007-02-04.

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5

Stein, Gary, and Avelino J. Gonzalez. "Learning in context: enhancing machine learning with context-based reasoning." Applied Intelligence 41, no. 3 (July 3, 2014): 709–24. http://dx.doi.org/10.1007/s10489-014-0550-0.

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6

Traxler, John. "Context in a wider context." Mobile Learning in Widening Contexts: Concepts and Cases 19, Mobile Learning (July 8, 2011): 1–16. http://dx.doi.org/10.21240/mpaed/19/2011.07.08.x.

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This paper attempts to review and reconsider the role of context in mobile learning and starts by outlining definitions of context-aware mobile learning as the technologies have become more mature, more robust and more widely available and as the notion of context has become progressively richer. The future role of context-aware mobile learning is considered within the context of the future of mobile learning as it moves from the challenges and opportunities of pedagogy and technology to the challenges and opportunities of policy, scale, sustainability, equity and engagement with augmented reality, «blended learning», «learner devices», «user-generated contexts» and the «internet of things». This is essentially a perspective on mobile learning, and other forms of technology-enhanced learning (TEL), where educators and their institutions set the agenda and manage change. There are, however, other perspectives on context. The increasing availability and use of smart-phones and other personal mobile devices with similar powerful functionality means that the experience of context for many people, in the form of personalized or location-based services, is an increasingly social and informal experience, rather than a specialist or educational experience. This is part of the transformative impact of mobility and connectedness on our societies brought about by these universal, ubiquitous and pervasive technologies. This paper contributes a revised understanding of context in the wider context (sic) of the transformations taking place in our societies. These are subtle but pervasive transformations of jobs, work and the economy, of our sense of time, space and place, of knowing and learning, and of community and identity. This leads to a radical reconsideration of context as the notions of ‹self› and ‹other› are transformed.
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Meyer, Margaret R., and Georgianna Diopoulos. "Anchored Learning in Context." Mathematics Teaching in the Middle School 8, no. 1 (September 2002): 16–21. http://dx.doi.org/10.5951/mtms.8.1.0016.

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O'Brien, Marion, and Xiufen Bi. "Language Learning in Context." Topics in Early Childhood Special Education 15, no. 2 (April 1995): 148–63. http://dx.doi.org/10.1177/027112149501500202.

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Göker, A. "Context learning in Okapi." Journal of Documentation 53, no. 1 (March 1997): 80–83. http://dx.doi.org/10.1108/eum0000000007194.

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Thüs, Hendrik, Mohamed Amine Chatti, Esra Yalcin, Christoph Pallasch, Bogdan Kyryliuk, Togrul Mageramov, and Ulrik Schroeder. "Mobile learning in context." International Journal of Technology Enhanced Learning 4, no. 5/6 (2012): 332. http://dx.doi.org/10.1504/ijtel.2012.051818.

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Дисертації з теми "In-Context learning"

1

Colborn, Matthew L. C. "Context learning in bumblebees." Thesis, University of Sussex, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.398787.

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2

Yao, Xin. "Word Learning in Context." The Ohio State University, 2010. http://rave.ohiolink.edu/etdc/view?acc_num=osu1291060246.

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3

Beagle, Donald. "The Learning Commons in Historical Context." 名古屋大学附属図書館研究開発室, 2009. http://hdl.handle.net/2237/14578.

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Smith, Jeremiah. "Learning in mobile context-aware applications." Thesis, Imperial College London, 2015. http://hdl.handle.net/10044/1/30633.

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This thesis explores and proposes solutions to the challenges in deploying context-aware systems that make decisions or take actions based on the predictions of a machine learner over long periods of time. In particular, this work focuses on mobile context-aware applications which are intrinsically personal, requiring a specific solution for each individual that takes into account user preferences and changes in user behaviour as time passes. While there is an abundance of research on mobile context-aware applications which employ machine learning, most does not address the three core challenges required to be deployable over indefinite periods of time. Namely, (1) user-friendly and longitudinal collection and labelling of data, (2) measuring a user's experienced performance and (3) adaptation to changes in a user's behaviour, also known as concept drift. This thesis addresses these challenges by introducing (1) an infer-and-confirm data collection strategy which passively collects data and infers data labels using the user's natural response to target events, (2) a weighted accuracy measure Aw as the objective function for underlying machine learners in mobile context-aware applications and (3) two training instance selection algorithms, Training Grid and Training Clusters which only forget data points in areas of the data space where newer evidence is available, moving away from the traditional time window based techniques. We also propose a new way of measuring concept drift indicating which type of concept drift adaption strategy is likely to be beneficial for any given dataset. This thesis also shows the extent to which the requirements posed by the use of machine learning in deployable mobile context-aware applications influences its overall design by evaluating a mobile context-aware application prototype called RingLearn, which was developed to mitigate disruptive incoming calls. Finally, we benchmark our training instance selection algorithms over 8 data corpuses including the RingLearn corpus collected over 16 weeks and the Device Analyzer corpus which logs several years of smartphone usage for a large set of users. Results show that our algorithms perform at least as well as state-of-the-art solutions and many times significantly better with performance delta ranging from -0.2% to +11.3% compared to the best existing solutions over our experiments.
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Gómez, Ardila Sergio Eduardo. "Learning design implementation in context-aware and adaptive mobile learning." Doctoral thesis, Universitat de Girona, 2013. http://hdl.handle.net/10803/116492.

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Mobile learning (m-learning) is still in its infancy, and great efforts should be made so as to investigate the potentials of an educational paradigm shift from the traditional one-size-fits-all teaching approaches to an adaptive learning that can be delivered via mobile devices. Thus, the next challenge has been identified from this implication: How learning design can be implemented so as to benefit from the m-learning characteristics and achieve adaptation and personalization of the individual learning process in different contexts? An important factor for achieving personalized and adaptive m-learning has been the pedagogically meaningful and technically feasible processing of learners’ contextual information. Therefore in this work, design and delivery of personalized educational scenarios are suggested to be re-thought so as to benefit from the affordances of mobile technologies and the learners’ context
El aprendizaje móvil (m-learning) se encuentra todavía en su infancia y grandes esfuerzos se deben hacer para investigar el cambio de paradigma educativo, desde la forma de enseñanza tradicional de 'un modelo único para todos' a un aprendizaje adaptativo que se pueda entregar a través de dispositivos móviles. De esta manera, el siguiente desafío ha sido identificado por esta implicación: ¿Cómo se puede implementar el diseño instruccional con el fin de beneficiarse de las características del m-learning y lograr la adaptación y personalización del proceso de aprendizaje personal en diferentes contextos? Un importante factor para lograr un m-learning personalizado y adaptable ha sido el procesamiento de la información contextual de los estudiantes. Por lo tanto, en este trabajo se sugiere que sean re-pensados el diseño y la entrega de escenarios educativos orientados a la personalización del aprendizaje y que se beneficien de las potencialidades de las tecnologías móviles y el contexto de los estudiantes
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Leung, Kit Hang. "Reflective learning in a Continuing Medical Education e-learning context." Thesis, McGill University, 2009. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=32613.

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Abstract The lack of a practical operational definition of "reflective learning" suitable for characterizing observations of performance is widely acknowledged. This definition is needed for observing and documenting reflective practice in research, education, and continuing professional development. The current exploratory research aims to operationalize "reflective learning" using a conceptual framework based on a comprehensive review of the relevant literature, and to validate this framework in the context of a medium-large scale study of brief self-directed e-learning activities in continuing medical education. Properties of reflective learning were identified in a literature review on reflective learning and higher order thinking. These properties were structured and represented in a conceptual framework, herein called the Reflective Learning Framework. Then, this framework was revised, and a related coding scheme was tested in a pilot study. The revised framework was validated in a qualitative multiple case study that involved 473 family physicians participating in writing comments and interviews. The written comments resulted in 1,776 comment cases, and 253 interview cases. A comment case was defined as one family physician reading, rating, and commenting on one research-based synopsis online. An interview case was defined as one family physician reading and rating a synopsis online, and then explaining the rationale of his/her rating. Using thematic analysis and the framework-related coding scheme, cognitive tasks observed in these cases were categorized. Then, using cross case analysis, frequency counts of each category in all cases were compared in matrices.
Résumé L'absence d'une définition opérationnelle de l'apprentissage réflexif est habituellement reconnue. Cette définition est nécessaire pour observer et documenter la pratique réflexive en recherche, en éducation et en développement professionnel continu. La présente recherche explicative vise à opérationnaliser l'apprentissage réflexif avec un cadre conceptuel et à valider ce cadre dans le contexte des activités de formation en ligne en éducation médicale continue (e-learning). Une revue de la littérature sur l'apprentissage réflexif et le « higher order thinking » a permis d'identifier les caractéristiques de l'apprentissage réflexif. Ces caractéristiques ont été organisées et représentées dans un cadre conceptuel (Reflective Learning Framework). Ce cadre conceptuel et la grille de codage correspondante ont été révisés lors d'une étude pilote. Le cadre conceptuel révisé a ensuite été validé par une étude qualitative de cas multiples qui incluait des cas « commentaires » fournis par 473 médecins de famille, et des cas « entrevues » fournis par 40 médecins de famille et six médecins spécialistes. Concernant les commentaires, un cas est défini comme étant un médecin de famille qui lit, évalue et commente, sur Internet, un synopsis d'article de recherche. Concernant les entrevues, un cas est défini comme étant un médecin de famille qui lit et évalue un synopsis sur Internet, et qui explique la logique sur laquelle repose son évaluation. Les commentaires écrits ont contribué à 1.776 cas « commentaires », et les transcriptions d'entrevues à 253 cas. Une analyse thématique fondée sur le cadre conceptuel et$
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Ta, Thi Han Hoan. "Self-regulated learning in a Vietnamese context." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1999. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape7/PQDD_0026/MQ51482.pdf.

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Koetsier, Jos. "Context-assisted learning in artifical neural networks." Thesis, University of the West of Scotland, 2003. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.269408.

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Lowry, Katherine Danielle. "Age and Context Dependency in Causal Learning." TopSCHOLAR®, 2015. http://digitalcommons.wku.edu/theses/1538.

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The ability to make associations between causal cues and outcomes is an important adaptive trait that allows us to properly prepare for an upcoming event. Encoding context is a type of associative processing; thus, context is also an important aspect of acquiring causal relationships. Context gives us additional information about how two events are related and allows us to be flexible in how we respond to causal cues. Research indicates that older adults exhibit an associative deficit as well as a deficit in contextual processing; therefore, it seems likely that these deficits are responsible for the deficit in older adults’ causal learning. The purpose of the current study was to more directly test how associative deficits related to older adults’ contextual processing affect their causal learning. Based on past research, it was hypothesized that older adults would be less likely than younger adults to acquire and use contextual information in causal learning. A causal learning scenario from Boddez, Baeyens, Hermans, and Beckers (2011) was used to test the hypothesis that older adults show deficits in contextual processing in a causal learning scenario. This task examined contextual processing using blocking and extinction. Participants went through eight blocks of trials in which they were exposed to various cues and outcomes. They provided expectancy ratings that indicated how likely they believed an outcome was to occur, and these ratings were used to assess age differences in use of contextual information in a causal learning scenario. As expected, both younger and older adults demonstrated blocking in that they assigned higher causal value to a previously trained target cue (A+) than to another cue (X) that was only presented in compound with cue A later in the task (i.e., AX+). Additionally, when tested in the context where the association was originally learned following extinction training (i.e., A-), the causal value of cue A decreased for all groups, even if extinction training took place in a different context. However, ratings for cue A decreased even more for younger adults whose extinction training took place in a different context when tested in their extinction context.
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Johansson, Samuel, and Karol Wojtulewicz. "Machine learning algorithms in a distributed context." Thesis, Linköpings universitet, Institutionen för datavetenskap, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-148920.

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Interest in distributed approaches to machine learning has increased significantly in recent years due to continuously increasing data sizes for training machine learning models. In this thesis we describe three popular machine learning algorithms: decision trees, Naive Bayes and support vector machines (SVM) and present existing ways of distributing them. We also perform experiments with decision trees distributed with bagging, boosting and hard data partitioning and evaluate them in terms of performance measures such as accuracy, F1 score and execution time. Our experiments show that the execution time of bagging and boosting increase linearly with the number of workers, and that boosting performs significantly better than bagging and hard data partitioning in terms of F1 score. The hard data partitioning algorithm works well for large datasets where the execution time decrease as the number of workers increase without any significant loss in accuracy or F1 score, while the algorithm performs poorly on small data with an increase in execution time and loss in accuracy and F1 score when the number of workers increase.
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Книги з теми "In-Context learning"

1

Duval, Erik, Steffan Ternier, and F. van Assche. Learning objects in context. Chesapeake, VA: Association for the Advancement of Computing in Education, 2009.

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2

Helen, Rainbird, Fuller Alison, and Munro Anne, eds. Workplace learning in context. New York, NY: Routledge, 2004.

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3

Geoffrey, Walford, and Massey Alexander, eds. Children learning in context. Stamford, Conn: JAI Press, 1998.

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4

Center for Occupational Research and Development (U.S.), ed. Algebra 1: Learning in context. 3rd ed. Waco, Texas: CORD Communications, 2009.

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5

Breiteig, T. Teaching and learning mathematics in context. New York: E. Horwood, 1993.

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6

Robert, Tinker, and Krajcik Joseph S, eds. Portable technologies: Science learning in context. New York: Kluwer Academic/Plenum Publishers, 2001.

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7

Peter, Jarvis. Adult learning in the social context. London: Croom Helm, 1987.

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Jarvis, P. Adult learning in the social context. London: Routledge & Kegan Paul, 1987.

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9

Paul, Walker. Writing in context: Composition in first-year learning communities. New York, New York: Hampton Press, Inc., 2013.

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10

Kirsten, Malmkjær, Williams John 1960-, and Summer Institute in English and Applied Linguistics (3rd : 1995 : University of Cambridge), eds. Context in language learning and language understanding. New York, NY: Cambridge University Press, 1998.

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Частини книг з теми "In-Context learning"

1

Johnson, Cynthia L. "Context and Machine Learning." In Context in Computing, 113–26. New York, NY: Springer New York, 2014. http://dx.doi.org/10.1007/978-1-4939-1887-4_8.

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Egbert, Joy. "10. Learning in context." In Teacher Education in CALL, 167–81. Amsterdam: John Benjamins Publishing Company, 2006. http://dx.doi.org/10.1075/lllt.14.15egb.

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Stirling, Diana. "Online Learning in Context." In Learners in a Changing Learning Landscape, 165–86. Dordrecht: Springer Netherlands, 2008. http://dx.doi.org/10.1007/978-1-4020-8299-3_8.

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Day, Marc. "Organisational learning, ambidexterity and strategic innovation." In Strategy in Context, 132–49. London: Routledge, 2024. http://dx.doi.org/10.4324/9781003400769-8.

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Artess, Jane. "Learning to be employable." In Graduate Careers in Context, 96–109. Abingdon, Oxon ; New York, NY : Routledge, 2018.: Routledge, 2018. http://dx.doi.org/10.4324/9780203732281-8.

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Forissier, Thomas, Jacqueline Bourdeau, Yves Mazabraud, and Roger Nkambou. "Computing the Context Effect for Science Learning." In Context in Computing, 255–69. New York, NY: Springer New York, 2014. http://dx.doi.org/10.1007/978-1-4939-1887-4_17.

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Hardison, Debra M. "The visual element in phonological perception and learning." In Phonology in Context, 135–58. London: Palgrave Macmillan UK, 2007. http://dx.doi.org/10.1057/9780230625396_6.

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Khunyakari, Ritesh. "Assessment and Learning in Indian Context." In Learning without Burden, 120–43. London: Routledge India, 2022. http://dx.doi.org/10.4324/9781003046059-7.

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Kleyn, Tatyana, and Tim Porter. "Return Migration in Context." In Living, Learning, and Languaging Across Borders, 3–20. New York: Routledge, 2021. http://dx.doi.org/10.4324/9780429340178-1.

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Tucker-Raymond, Eli, and Brian E. Gravel. "Learning in a Professional Context." In STEM Literacies in Makerspaces, 16–33. New York, NY : Routledge, 2019.: Routledge, 2019. http://dx.doi.org/10.4324/9781351256728-2.

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Тези доповідей конференцій з теми "In-Context learning"

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Safaee, Mehdi, Aryan Mikaeili, Or Patashnik, Daniel Cohen-Or, and Ali Mahdavi-Amiri. "CLiC: Concept Learning in Context." In 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 6924–33. IEEE, 2024. http://dx.doi.org/10.1109/cvpr52733.2024.00661.

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Wang, Xinshun, Zhongbin Fang, Xia Li, Xiangtai Li, Chen Chen, and Mengyuan Liu. "Skeleton-in-Context: Unified Skeleton Sequence Modeling with In-Context Learning." In 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2436–46. IEEE, 2024. http://dx.doi.org/10.1109/cvpr52733.2024.00236.

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Zhou, Anlai, Sunshine Jiang, Yifei Liu, Yiquan Wu, Kun Kuang, and Jun Xiao. "Latent Learningscape Guided In-context Learning." In Findings of the Association for Computational Linguistics ACL 2024, 8090–101. Stroudsburg, PA, USA: Association for Computational Linguistics, 2024. http://dx.doi.org/10.18653/v1/2024.findings-acl.482.

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Santos, Sarah, Travis Breaux, Thomas Norton, Sara Haghighi, and Sepideh Ghanavati. "Requirements Satisfiability with In-Context Learning." In 2024 IEEE 32nd International Requirements Engineering Conference (RE), 168–79. IEEE, 2024. http://dx.doi.org/10.1109/re59067.2024.00025.

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Long, Do, Yiran Zhao, Hannah Brown, Yuxi Xie, James Zhao, Nancy Chen, Kenji Kawaguchi, Michael Shieh, and Junxian He. "Prompt Optimization via Adversarial In-Context Learning." In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 7308–27. Stroudsburg, PA, USA: Association for Computational Linguistics, 2024. http://dx.doi.org/10.18653/v1/2024.acl-long.395.

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Baldassini, Folco Bertini, Mustafa Shukor, Matthieu Cord, Laure Soulier, and Benjamin Piwowarski. "What Makes Multimodal In-Context Learning Work?" In 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 1539–50. IEEE, 2024. http://dx.doi.org/10.1109/cvprw63382.2024.00161.

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Peng, Keqin, Liang Ding, Yancheng Yuan, Xuebo Liu, Min Zhang, Yuanxin Ouyang, and Dacheng Tao. "Revisiting Demonstration Selection Strategies in In-Context Learning." In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 9090–101. Stroudsburg, PA, USA: Association for Computational Linguistics, 2024. http://dx.doi.org/10.18653/v1/2024.acl-long.492.

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Luckin, Rosemary. "Participatory learning in context." In the 9th international conference. Morristown, NJ, USA: Association for Computational Linguistics, 2009. http://dx.doi.org/10.3115/1599503.1599507.

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Grujić, Tatjana. "L2 TENSE TRANSFER IN EFL LEARNING." In SCIENCE AND TEACHING IN EDUCATIONAL CONTEXT. FACULTY OF EDUCATION IN UŽICE, UNIVERSITY OF KRAGUJEVAC, 2020. http://dx.doi.org/10.46793/stec20.441g.

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Анотація:
In second language acquisition (SLA) transfer is predominantly explored as either positive or negative influence of learners’ first language (L1) on their second/foreign language (L2) performance. Studies in this field serve not only to describe the learner’s interlanguage, but also to inform, improve and refine foreign language teaching. However, the scope of SLA studies is such that it leaves the other transfer direction under-researched (L2 to L1), assuming that once the learner’s L1 system has fully developed, their L1 competence will not be subject to change. More recent studies of adult bilinguals have shown a bidirectional interaction between the two linguistic systems: not only does L1 influence L2, but L2 influences L1 as well. In this study, conducted among adult students of English (B2 to C1 level language users, according to CEFR), we examine the influence of English as a foreign language upon Serbian as a native tongue in terms of tense transfer. More precisely, the study explores how the subjects interpret and translate the secondary meanings of the English past tense. The basic meaning of the past tense is to locate an event (or state) in the past. However, in its secondary meanings (backshift past in reported clauses, counterfactual present in adverbial clauses of condition and ‘past subjunctive’ when expressing wishes and regrets) it does not refer to the past time. The error analysis of students’ English to Serbian translations provides evidence of L2 influence: learners tend to use the Serbian past rather than the present tense in their translations. Pedagogical implications of this study of misuse of L1 tense include focusing on explicit corrective feedback and polishing instructional materials.
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Cotič, Mara, Darjo Felda, and Amalija Žakelj. "TAXONOMY OF LEARNING OBJECTIVES AT ELEMENTARY LEVEL." In SCIENCE AND TEACHING IN EDUCATIONAL CONTEXT. FACULTY OF EDUCATION IN UŽICE, UNIVERSITY OF KRAGUJEVAC, 2020. http://dx.doi.org/10.46793/stec20.335c.

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Looking for an answer to the question what knowledge represents the centre of gravity in teaching and learning and thus also in testing and assessing knowledge, as well as in the interpretation of students’ achievements taxonomies of learningobjectives for the cognitive area can be of assistance. In education sciences there are several taxonomies of cognitive knowledge (Bloom, Marzano, Gagne). Taxonomy is derived from basic cognitive – mental processes that are arranged in a hierarchic relationship, namely from the lowest – the simplest to the highest – the most complex process. The present paper represents an introduction to Bloom’s, Gagne’s, and Marzano’s taxonomies. Bloom’s taxonomy is one of the best known classifications of learning objectives, where Bloom and associates have formed a taxonomy of cognitive, conative, and psycho-motoric learning objectives. In the cognitive area the following degrees have been defined: remembering, understanding, applying, analysing, synthesising, and evaluating. Gagne’s classification of knowledge classifies the achievements of learners into: basic and conceptual knowledge, procedural knowledge, and problem solving knowledge. Marzano’s taxonomy distinguishes between content and lifelong or process knowledge, which are further divided into complex thinking, data processing, communication, cooperation in the group, and development of mental habits.
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Звіти організацій з теми "In-Context learning"

1

Bykova, Tatyana B., Mykola V. Ivashchenko, Darja A. Kassim, and Vasyl I. Kovalchuk. Blended learning in the context of digitalization. [б. в.], June 2021. http://dx.doi.org/10.31812/123456789/4441.

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The realities of digitalization require changes in strategies for choosing educational technologies. The modern educational process is not possible without the use of digital technologies. Digital technologies have led to the arising and development of blended learning. However, its effectiveness is determined not only by technology. The human factor receives special attention in this direction. Analysis of the World Development Report 2016: Digital Dividends allows us to identify digital competence as a necessary condition for the successful use of digital technologies, and hence blended learning. Learning interactions designing in the process of implementing blended learning requires timely diagnosis of the level of digital competence. A popular tool for this is the Digital Competence Framework for Citizens. To clarify the peculiarities of its use was made an analysis of the experimental implementation results of blended learning in the industrial training in sewing for intended masters. During the research, it was revealed that the most important digital competence areas for the variable learning establishment in the training of future professionals are Information and data literacy, Communication and collaboration and Problem solving. In addition, competence for area Problem solving conduce to increase the level of competence for all other areas. The level of digital competence of the subjects mainly coincide to the characteristics of basic and secondary levels. The obtained data clarified the reasons for the difficulties, decrease motivation and cognitive activity that occur among students using distance courses-resources learning designed for blended learning. Thus, the use of the Digital Competence Framework for Citizens at the initial stage of implementing blended learning can make a rational choice of strategies for combining face-to-face and distance learning technologies.
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2

Ganapathysubramanian, Baskar. Context-Aware Learning for Inverse Design in Photovoltaics. Office of Scientific and Technical Information (OSTI), May 2022. http://dx.doi.org/10.2172/2371715.

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Duden, Mary. Teacher Learning in the Context of Students’ Mathematical Thinking. Portland State University Library, January 2000. http://dx.doi.org/10.15760/etd.7367.

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Bock, Geoffrey. Putting E-Learning in the Context of Customers Scenarios. Boston, MA: Patricia Seybold Group, September 2004. http://dx.doi.org/10.1571/psgp9-24-04cc.

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Halverstadt, Ashleigh Halverstadt. Learning in Context: Reflections on the Education Team’s Approach to Evaluation. San Francisco, CA United States: S. D. Bechtel, Jr. Foundation, April 2020. http://dx.doi.org/10.15868/socialsector.37444.

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Mittal, Nidhi, Nipunika Perera, and Outi Korkeala. Learning Materials: Leaving no-one behind in the climate and environment context. Evidence on Demand, December 2016. http://dx.doi.org/10.12774/eod_tg.october2016.mittal.

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Roh, Anthony M. Russian Organizational Learning in the Context of the Afghanistan and Chechnya Counterinsurgencies. Fort Belvoir, VA: Defense Technical Information Center, December 2014. http://dx.doi.org/10.21236/ada614153.

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8

Bret Linford, Bret Linford. Learning beyond the textbook: Developing dialect-specific grammar in a study abroad context. Experiment, November 2013. http://dx.doi.org/10.18258/1568.

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Бакум, З. П., and В. В. Ткачук. Mining Engineers Training in Context of Innovative System of Ukraine. Криворізький державний педагогічний університет, 2014. http://dx.doi.org/10.31812/0564/425.

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Анотація:
The paper clarifies the process of a mining engineer training in terms of the innovation system of Ukraine. The authors analyze a number of normative documents concerning innovative activity in Ukraine in general and mining business in particular. In the process of implementation of innovations into mining industrial complex urgent problems are defined. The methodology of information and communication technologies (electronic, distance and mobile studies) usage in engineers training within the conditions of university education is offered. It is marked that the worked out methodology finds its practical introduction: e-learning involves creation of the portal "Electronic mentor"; distance learning is presented in the study of professional disciplines as an example of the course "Сomputer Technologies in Mining"; mobile learning is considered as an example of discipline "Computer Science and Engineering".
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Pikilnyak, Andrey V., Nadia M. Stetsenko, Volodymyr P. Stetsenko, Tetiana V. Bondarenko, and Halyna V. Tkachuk. Comparative analysis of online dictionaries in the context of the digital transformation of education. [б. в.], June 2021. http://dx.doi.org/10.31812/123456789/4431.

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The article is devoted to a comparative analysis of popular online dictionaries and an overview of the main tools of these resources to study a language. The use of dictionaries in learning a foreign language is an important step to understanding the language. The effectiveness of this process increases with the use of online dictionaries, which have a lot of tools for improving the educational process. Based on the Alexa Internet resource it was found the most popular online dictionaries: Cambridge Dictionary, Wordreference, Merriam–Webster, Wiktionary, TheFreeDictionary, Dictionary.com, Glosbe, Collins Dictionary, Longman Dictionary, Oxford Dictionary. As a result of the deep analysis of these online dictionaries, we found out they have the next standard functions like the word explanations, transcription, audio pronounce, semantic connections, and examples of use. In propose dictionaries, we also found out the additional tools of learning foreign languages (mostly English) that can be effective. In general, we described sixteen functions of the online platforms for learning that can be useful in learning a foreign language. We have compiled a comparison table based on the next functions: machine translation, multilingualism, a video of pronunciation, an image of a word, discussion, collaborative edit, the rank of words, hints, learning tools, thesaurus, paid services, sharing content, hyperlinks in a definition, registration, lists of words, mobile version, etc. Based on the additional tools of online dictionaries we created a diagram that shows the functionality of analyzed platforms.
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