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Relevant bibliographies by topics / Smart Learning Environments

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Academic literature on the topic 'Smart Learning Environments'

Author: Grafiati

Published: 4 June 2021

Last updated: 26 July 2025

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Journal articles on the topic "Smart Learning Environments"

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Montebello, Matthew. "Smart Ubiquitous Learning Environments." International Journal of Education (IJE) 5, no. 4 (2017): 17–24. http://dx.doi.org/10.5121/ije.2017.5402.

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Anjomshoaa, Amin, and Edward Curry. "Transfer Learning in Smart Environments." Machine Learning and Knowledge Extraction 3, no. 2 (2021): 318–32. http://dx.doi.org/10.3390/make3020016.

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The knowledge embodied in cognitive models of smart environments, such as machine learning models, is commonly associated with time-consuming and costly processes such as large-scale data collection, data labeling, network training, and fine-tuning of models. Sharing and reuse of these elaborated resources between intelligent systems of different environments, which is known as transfer learning, would facilitate the adoption of cognitive services for the users and accelerate the uptake of intelligent systems in smart building and smart city applications. Currently, machine learning processes

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Catala, Alejandro, Javier Jaen, Patricia Pons, and Fernando Garcia-Sanjuan. "Towards Creative Smart Learning Environments." International Journal of Creative Interfaces and Computer Graphics 6, no. 1 (2015): 56–71. http://dx.doi.org/10.4018/ijcicg.2015010104.

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The creation of artefacts and entities to be used in simulated worlds and games has been used in practice to foster creativity in informal activities. Motivational issues and entertainment have therefore been combined along with creative tasks. The systems with higher complexity and more creative capabilities are usually based on WIMP interfaces, which negatively impact on the role of collaboration and active participation. This paper summarizes the main findings and the authors' experiences in the project CreateWorlds, which relies on an interactive tabletop interface in order to support the

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Mutizwa, Melissa Rutendo, Fezile Ozdamli, and Damla Karagozlu. "Smart Learning Environments during Pandemic." Trends in Higher Education 2, no. 1 (2023): 16–28. http://dx.doi.org/10.3390/higheredu2010002.

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Education was one of the many day-to-day activities affected by the novel coronavirus pandemic (COVID-19). When countries began to shut down in April 2020, nationwide lockdowns, self-isolation, or quarantine became the new normal for everyone. The education sector was kept alive by smart learning environments. Now, more than ever, online learning and tools were implemented. This study aims to systematically review the literature on the impact of the pandemic on smart learning environments. The method adopted in this paper is a systematic literature review, and it will use the PRISMA technique.

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Gao, Jing, and Normala Ismail. "A Systematic Literature Review on the Relationship Between Smart Learning Environments and Learning Performance in Higher Education." Journal of Public Administration and Governance 14, no. 2S (2025): 54. https://doi.org/10.5296/jpag.v14i2s.22589.

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Smart learning environments, as an advanced educational technology, are widely acknowledged as a crucial component of contemporary educational systems. This study aims to examine the current literature concerning smart learning environments and their influence on learning performance in higher education. The writers examine papers published in ERIC over the past decade, focusing on their content, reasoning, terminology, methodology, and findings. This comprehensive literature review offers educators practical information on efficiently utilizing smart learning environments and provides academi

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Embarak, Ossama H., Maryam J. Almesmari, and Fatima R. Aldarmaki. "Smart Learning in the Ecosystem: Examines Smart Learning Structural Design Features Considering IoT and IoB." Journal of Intelligent Systems and Internet of Things 7, no. 1 (2022): 20–28. http://dx.doi.org/10.54216/jisiot.070102.

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The Internet of Things (IoT), IoT-Education, and smartness are emerging technology used in Industry 4.0 to enable smarter education systems that can be adapted to different learners. Using IoT as an acceptable and useable infrastructure is one of the leaders' innovative strategies. It is an intelligence enabler that will be integrated into many essential parts of the future world. This study looks at the key elements of smart learning structural design, such as IoT and IoB (internet of behavior), as well as the major issues that must be addressed when creating smart educational environments th

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Kinshuk, Nian-Shing Chen, I.-Ling Cheng, and Sie Wai Chew. "Evolution Is not enough: Revolutionizing Current Learning Environments to Smart Learning Environments." International Journal of Artificial Intelligence in Education 26, no. 2 (2016): 561–81. http://dx.doi.org/10.1007/s40593-016-0108-x.

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Al-Nabhani, Bayan Khalfan Saif, and Supriya Pulparambil. "Smart Learning Environments: A Comprehensive Review on Artificial Intelligence and Learning Theories." International Journal of Research and Scientific Innovation XI, no. XIV (2024): 96–103. http://dx.doi.org/10.51244/ijrsi.2024.1114sdaico11.

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The term “smart learning” is now commonly used to describe online education. It shows how learners can use knowledge and skills more easily, successfully, and simply because of modern technologies. The goal is to create a smart learning environment that is relevant, timely, and efficient, allowing students to learn more effectively and successfully. By setting realistic goals, focusing on the big picture, and ensuring a clear timeline for task completion, a smart learning environment can provide a more engaging and efficient educational experience for students. The term “smart technology” desc

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Tageldin, Laila, and Hein Venter. "Machine-Learning Forensics: State of the Art in the Use of Machine-Learning Techniques for Digital Forensic Investigations within Smart Environments." Applied Sciences 13, no. 18 (2023): 10169. http://dx.doi.org/10.3390/app131810169.

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Recently, a world-wide trend has been observed that there is widespread adoption across all fields to embrace smart environments and automation. Smart environments include a wide variety of Internet-of-Things (IoT) devices, so many challenges face conventional digital forensic investigation (DFI) in such environments. These challenges include data heterogeneity, data distribution, and massive amounts of data, which exceed digital forensic (DF) investigators’ human capabilities to deal with all of these challenges within a short period of time. Furthermore, they significantly slow down or even

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Ullah, Nazir, Waleed Mugahed Al-Rahmi, Ahmed Ibrahim Alzahrani, Osama Alfarraj, and Fahad Mohammed Alblehai. "Blockchain Technology Adoption in Smart Learning Environments." Sustainability 13, no. 4 (2021): 1801. http://dx.doi.org/10.3390/su13041801.

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The conventional education system in developing countries has been enhanced recently by implementing the latest technology of distributed ledger. Disruptive technology is a fundamental requirement for greater accountability and visibility. We explored the key factors affecting the intentions of educational institutions to use blockchain technology for e-learning. This study proposed an expanded model of Technology Acceptance Model by integrating the diffusion of innovation theory. Based on an online survey, the conceptual model was tested and validated using structural equation modeling. The r

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Dissertations / Theses on the topic "Smart Learning Environments"

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ALIBERTI, ALESSANDRO. "Machine learning techniques to forecast non-linear trends in smart environments." Doctoral thesis, Politecnico di Torino, 2020. http://hdl.handle.net/11583/2846613.

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Freigang, Sirkka, Lars Schlenker, and Thomas Köhler. "Gestaltung von Smart Learning Environments in der betrieblichen Weiterbildung als interdisziplinäre Herausforderung." Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden, 2018. http://nbn-resolving.de/urn:nbn:de:bsz:14-qucosa-234365.

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Vor dem Hintergrund aktueller bildungswissenschaftlicher Diskussionen zur digitalen Transformation setzt sich der Beitrag mit der Rolle von Smart Learning Environments (SLEs) in der betrieblichen Weiterbildung und der Notwendigkeit deren interdisziplinärer Gestaltung auseinander. Für eine taugliche, auf den Nutzer fokussierte Gestaltung von SLEs müssen neuartige Konzepte und Modelle entwickelt werden, die den komplexen Anforderungen des 21. Jahrhunderts entsprechen. Der Beitrag beschreibt einen interdisziplinären Forschungszugang zur Gestaltung von SLEs und diskutiert aufbauend ein ganzheitlic

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Freigang, Sirkka, Lars Schlenker, and Thomas Köhler. "Gestaltung von Smart Learning Environments in der betrieblichen Weiterbildung als interdisziplinäre Herausforderung." TUDpress, 2017. https://tud.qucosa.de/id/qucosa%3A30885.

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Vor dem Hintergrund aktueller bildungswissenschaftlicher Diskussionen zur digitalen Transformation setzt sich der Beitrag mit der Rolle von Smart Learning Environments (SLEs) in der betrieblichen Weiterbildung und der Notwendigkeit deren interdisziplinärer Gestaltung auseinander. Für eine taugliche, auf den Nutzer fokussierte Gestaltung von SLEs müssen neuartige Konzepte und Modelle entwickelt werden, die den komplexen Anforderungen des 21. Jahrhunderts entsprechen. Der Beitrag beschreibt einen interdisziplinären Forschungszugang zur Gestaltung von SLEs und diskutiert aufbauend ein ganzheitlic

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Pushpagiri, Vara Prashanth. "A Java-based Smart Object Model for use in Digital Learning Environments." Thesis, Virginia Tech, 2003. http://hdl.handle.net/10919/34048.

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The last decade has seen the scope of digital library usage extend from data warehousing and other common library services to building quality collections of electronic resources and providing web-based information retrieval mechanisms for distributed learning. This is clear from the number of ongoing research initiatives aiming to provide dynamic learning environments. A major task in providing learning environments is to define a resource model (learning object). The flexibility of the learning object model determines the quality of the learning environment. Further, dynamic environments

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Casserfelt, Karl. "A Deep Learning Approach to Video Processing for Scene Recognition in Smart Office Environments." Thesis, Malmö universitet, Fakulteten för teknik och samhälle (TS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:mau:diva-20429.

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The field of computer vision, where the goal is to allow computer systems to interpret and understand image data, has in recent years seen great advances with the emergence of deep learning. Deep learning, a technique that emulates the information processing of the human brain, has been shown to almost solve the problem of object recognition in image data. One of the next big challenges in computer vision is to allow computers to not only recognize objects, but also activities. This study is an exploration of the capabilities of deep learning for the specific problem area of activity recogniti

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Al-Khafaji, Alaa. "The development of a theoretical framework for designing smart and ubiquitous learning environments for outdoor cultural heritage." Thesis, University of Portsmouth, 2018. https://researchportal.port.ac.uk/portal/en/theses/the-development-of-a-theoretical-framework-for-designing-smart-and-ubiquitous-learning-environments-for-outdoor-cultural-heritage(f9216916-8aac-4547-b276-51d94358e7de).html.

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The work presented in this thesis focuses on exploring the potential of the use and development of mobile location-based services at outdoor cultural heritage sites. This PhD research investigated how people use mobile and wearable technologies for learning purposes with respect to cultural heritage sites. A user-centred design approach was adopted in this thesis using the socio-cognitive engineering methodology. Three empirical studies (field studies) were conducted with the aim of capturing users’ requirements adopting mixed methods. The studies were conducted sequentially using focus group,

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Johansson, Alexander, and Oscar Sandberg. "A COMPARATIVE STUDY OF DEEP-LEARNING APPROACHES FOR ACTIVITY RECOGNITION USING SENSOR DATA IN SMART OFFICE ENVIRONMENTS." Thesis, Malmö universitet, Fakulteten för teknik och samhälle (TS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:mau:diva-20928.

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Syftet med studien är att jämföra tre deep learning nätverk med varandra för att ta reda på vilket nätverk som kan producera den högsta uppmätta noggrannheten. Noggrannheten mäts genom att nätverken försöker förutspå antalet personer som vistas i rummet där observation äger rum. Utöver att jämföra de tre djupinlärningsnätverk med varandra, kommer vi även att jämföra dem med en traditionell metoder inom maskininlärning - i syfte för att ta reda på ifall djupinlärningsnätverken presterar bättre än vad traditionella metoder gör. I studien används design and creation. Design and creation är en for

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Mufida, Miratul Khusna. "Deep learning for continuous parking occupancy forecasting in urban environments." Electronic Thesis or Diss., Valenciennes, Université Polytechnique Hauts-de-France, 2023. http://www.theses.fr/2023UPHF0024.

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L'apprentissage profond a été largement adopté dans divers domaines en raison de sa capacité à extraire des caractéristiques complexes à partir de grandes quantités de données. Dans cette thèse, nous proposons une approche basée sur l'apprentissage profond pour la prédiction continue de l'occupation des parkiL'apprentissage profond a été largement adopté dans divers domaines en raison de sa capacité à extraire des caractéristiques complexes à partir de grandes quantités de données. Dans cette thèse, nous proposons une approche basée sur l'apprentissage profond pour la prédiction continue de l'

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Ventura, Daniela. "A New Paradigm for Collaborative Smart Objects to Enable the Development of User-centric Services in Pervasive Environments." Doctoral thesis, Università di Catania, 2016. http://hdl.handle.net/10761/4086.

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In the near future, people will not be the only consumers of Web content, but also an increasing number of machines will be able to independently search and interpret data received from web servers in order to perform tasks for users. If a machine is an Internet-connected everyday object and its functionalities can be remotely invoked through REST API, then such machine will be part of the Web of Things. In addition to core features, objects will be augmented with sensing and adaptive capabilities, reasoning and decision-making abilities, and, as consequence, intelligence will be transferred t

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Mogas, Recalde Jordi. "Smart Classrooms i l'adveniment de la Quarta Revolució Industrial: anàlisi dels factors clau per al disseny d'aules intel·ligents." Doctoral thesis, Universitat Rovira i Virgili, 2021. http://hdl.handle.net/10803/670956.

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Aquest treball neix amb l’objectiu de descriure les aules intel·ligents analitzant-ne els elements diferenciadors, les dimensions i les interrelacions, per comprendre quines són les tendències que poden proporcionar millores en els processos d’ensenyament-aprenentatge dels estudiants del segle XXI. Per fer-ho, s’utilitza una metodologia d’investigació exploratòria que permet recollir dades i concatenar resultats per ampliar-ne el marc referencial. Atenent que el present document compendia un total de dotze publicacions, les tècniques i els instruments de recerca utilitzats són variats, tant de

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Books on the topic "Smart Learning Environments"

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Chang, Maiga, and Yanyan Li, eds. Smart Learning Environments. Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-662-44447-4.

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Marques, Gonçalo, Alfonso González-Briones, and José Manuel Molina López, eds. Machine Learning for Smart Environments/Cities. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-97516-6.

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Sampson, Demetrios G., Dirk Ifenthaler, and Pedro Isaías, eds. Smart Learning Environments in the Post Pandemic Era. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-54207-7.

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Tanwar, Sarvesh, Sumit Badotra, and Ajay Rana. Machine Learning, Blockchain, and Cyber Security in Smart Environments. Chapman and Hall/CRC, 2022. http://dx.doi.org/10.1201/9781003240310.

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Cheung, Simon K. S., Richard Li, Kongkiti Phusavat, Naraphorn Paoprasert, and Lam‑For Kwok, eds. Blended Learning. Education in a Smart Learning Environment. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-51968-1.

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Albaji, Ali Othman. Machine Learning for Environmental Noise Classification in Smart Cities. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-54667-9.

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Li, Yanyan, and Maiga Chang. Smart Learning Environments. Springer Berlin / Heidelberg, 2014.

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Li, Yanyan, and Maiga Chang. Smart Learning Environments. Springer London, Limited, 2014.

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Li, Yanyan, and Maiga Chang. Smart Learning Environments. Springer, 2016.

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Singh, Abtar Darshan, Shriram Raghunathan, Edward Robeck, and Bibhya Sharma. Cases on Smart Learning Environments. IGI Global, 2018.

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Book chapters on the topic "Smart Learning Environments"

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Rosak-Szyrocka, Joanna. "Smart Learning Environments." In Smart Universities in Smart Cities. Routledge, 2025. https://doi.org/10.4324/9781003621829-5.

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Nagao, Katashi. "Smart Learning Environments." In Artificial Intelligence Accelerates Human Learning. Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-6175-3_5.

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Mishra, Prerna, and Urmila Shrawankar. "Smart Learning Environments." In Cloud Computing for Smart Education and Collaborative Learning. Chapman and Hall/CRC, 2025. https://doi.org/10.1201/9781003472537-2.

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El-Bishouty, Moushir M., Ting-Wen Chang, Renan Lima, Mohamed B. Thaha, Kinshuk, and Sabine Graf. "Analyzing Learner Characteristics and Courses Based on Cognitive Abilities, Learning Styles, and Context." In Smart Learning Environments. Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-662-44447-4_1.

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Fan, Ming-Xiang, Rita Kuo, Maiga Chang, and Jia-Sheng Heh. "Story-Based Virtual Experiment Environment." In Smart Learning Environments. Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-662-44447-4_10.

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Santos, Marc Ericson C., Mitsuaki Terawaki, Takafumi Taketomi, Goshiro Yamamoto, and Hirokazu Kato. "Development of Handheld Augmented Reality X-Ray for K-12 Settings." In Smart Learning Environments. Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-662-44447-4_11.

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Kumar, Vive, Kinshuk, Thamaraiselvi Somasundaram, et al. "An Approach to Measure Coding Competency Evolution." In Smart Learning Environments. Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-662-44447-4_2.

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Gao, Yan, and Dunwei Wen. "Semantic Similarity-Enhanced Topic Models for Document Analysis." In Smart Learning Environments. Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-662-44447-4_3.

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Li, Yanyan, Shaoqian Ma, and Ronghuai Huang. "Social Context Analysis for Topic-Specific Expert Finding in Online Learning Communities." In Smart Learning Environments. Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-662-44447-4_4.

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Jong, Morris S. Y. "Context-Aware Geography Field Trip with EagleEye: Teachers’ First Experience." In Smart Learning Environments. Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-662-44447-4_5.

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Conference papers on the topic "Smart Learning Environments"

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de Souza, Álvaro Antônio Fonseca, and Daniel F. Macedo. "Comfort Personalisation in Smart Environments Using Transfer Learning." In 2025 Global Information Infrastructure and Networking Symposium (GIIS). IEEE, 2025. https://doi.org/10.1109/giis64151.2025.10921768.

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Kumar, Prem, Priyanshu Negi, Priyanka Sharma, and Pratham Mittal. "IoT-Enabled Smart Classrooms for Enhanced Learning Environments." In 2025 2nd International Conference on Computational Intelligence, Communication Technology and Networking (CICTN). IEEE, 2025. https://doi.org/10.1109/cictn64563.2025.10932356.

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Wang, Kegang, Yantao Wei, Jiankai Tang, et al. "Camera-Based HRV Prediction for Remote Learning Environments." In 2024 IEEE Smart World Congress (SWC). IEEE, 2024. https://doi.org/10.1109/swc62898.2024.00185.

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Hamid, Soomaiya, and Narmeen Zakaria Bawany. "Federated Learning for Enhanced Intrusion Detection in Smart City Environments." In 2024 18th International Conference on Open Source Systems and Technologies (ICOSST). IEEE, 2024. https://doi.org/10.1109/icosst64562.2024.10871154.

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Delgado Kloos, Carlos, Juan Asensio-Pérez, Davinia Hernández-Leo, et al. "GENIE Learn: Human-Centered Generative AI-Enhanced Smart Learning Environments." In 17th International Conference on Computer Supported Education. SCITEPRESS - Science and Technology Publications, 2025. https://doi.org/10.5220/0013076000003932.

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Wattacheril, Cherin Yacoob, G. R. Hemalakshmi, Ashin Murugan, P. N. V. Abhiram, Harisankar G, and Ansu M. George. "Machine Learning-Based Threat Detection in Crowded Environments." In 2024 International Conference on Smart Technologies for Sustainable Development Goals (ICSTSDG). IEEE, 2024. https://doi.org/10.1109/icstsdg61998.2024.11026626.

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Tirchas, Panagiotis, and George T. Karetsos. "Machine Learning Enabled Integrated Sensing and Communication for Smart 6G Environments." In 2025 28th Conference on Innovation in Clouds, Internet and Networks (ICIN). IEEE, 2025. https://doi.org/10.1109/icin64016.2025.10942988.

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Singh, Preet, Taniya Hasija, and KR Ramkumar. "Scalable Deep Learning Techniques for Smart Waste Segregation in Urban Environments." In 2024 International BIT Conference (BITCON). IEEE, 2024. https://doi.org/10.1109/bitcon63716.2024.10984510.

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Pyo, Changwoo, Ibuka Kazuo, Murakami Homare, Hirokazu Sawada, and Takeshi Matsumura. "Deep Learning-based Local 5G Radio Estimation for Smart Factory Environments." In 2024 27th International Symposium on Wireless Personal Multimedia Communications (WPMC). IEEE, 2024. https://doi.org/10.1109/wpmc63271.2024.10863803.

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Malathi, P., E. Dilipkumar, J. Rajasubha, AS Yokesh, L. Priya, and H. Anwer Basha. "Optimizing Resource Allocation in Cloud Environments Using Reinforcement Learning." In 2024 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems (ICSES). IEEE, 2024. https://doi.org/10.1109/icses63760.2024.10910650.

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Reports on the topic "Smart Learning Environments"

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Pasupuleti, Murali Krishna. Next-Generation Extended Reality (XR): A Unified Framework for Integrating AR, VR, and AI-driven Immersive Technologies. National Education Services, 2025. https://doi.org/10.62311/nesx/rrv325.

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Abstract: Extended Reality (XR), encompassing Augmented Reality (AR), Virtual Reality (VR), and Mixed Reality (MR), is evolving into a transformative technology with applications in healthcare, education, industrial training, smart cities, and entertainment. This research presents a unified framework integrating AI-driven XR technologies with computer vision, deep learning, cloud computing, and 5G connectivity to enhance immersion, interactivity, and scalability. AI-powered neural rendering, real-time physics simulation, spatial computing, and gesture recognition enable more realistic and adap

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Liu, Tairan. Addressing Urban Traffic Congestion: A Deep Reinforcement Learning-Based Approach. Mineta Transportation Institute, 2025. https://doi.org/10.31979/mti.2025.2322.

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In an innovative venture, the research team embarked on a mission to redefine urban traffic flow by introducing an automated way to manage traffic light timings. This project integrates two critical technologies, Deep Q-Networks (DQN) and Auto-encoders, into reinforcement learning, with the goal of making traffic smoother and reducing the all-too-common road congestion in simulated city environments. Deep Q-Networks (DQN) are a form of reinforcement learning algorithms that learns the best actions to take in various situations through trial and error. Auto-encoders, on the other hand, are tool

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Shukla, Indu, Rajeev Agrawal, Kelly Ervin, and Jonathan Boone. AI on digital twin of facility captured by reality scans. Engineer Research and Development Center (U.S.), 2023. http://dx.doi.org/10.21079/11681/47850.

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The power of artificial intelligence (AI) coupled with optimization algorithms can be linked to data-rich digital twin models to perform predictive analysis to make better informed decisions about installation operations and quality of life for the warfighters. In the current research, we developed AI connected lifecycle building information models through the creation of a data informed smart digital twin of one of US Army Corps of Engineers (USACE) buildings as our test case. Digital twin (DT) technology involves creating a virtual representation of a physical entity. Digital twin is created

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Cherniavskyi, Ruslan, Yaroslav Krainyk, and Anzhela Boiko. Modeling university environment: means and applications for university education. [б. в.], 2020. http://dx.doi.org/10.31812/123456789/3742.

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In the paper, we establish an investigation on the development of university 3D-model and its possible applications for educational and research fields. We assume that 3D-model of university can help in various scenarios and should be used to adopt modern immersing technologies into to university processes. Different means are employed for the development of the model. Bottom-up approach for using these means and their connection with each other are shown in the work. Then, details of the 3D-model design process are provided with peculiarities related to the university building location and co

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Fowler, Camilla. Automation in transport - Leading the UK to a driverless future. TRL, 2021. http://dx.doi.org/10.58446/tawj9464.

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The gap between technology development and automated vehicle deployment has been underestimated and the challenges involved with delivering autonomy have been far greater and more complex than first envisaged. TRL believe that in order for the UK to achieve its potential for automation in transport, the following activities are key in overcoming these challenges: Develop a UK regulatory approval system that enables the safe and secure deployment of automated vehicles in the future. A flexible and responsive regulatory system is needed that can enable innovation by streamlining entry into emerg

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Papadakis, Stamatios, Арнольд Юхимович Ків, Hennadiy M. Kravtsov, et al. Revolutionizing education: using computer simulation and cloud-based smart technology to facilitate successful open learning. Криворізький державний педагогічний університет, 2023. http://dx.doi.org/10.31812/123456789/7375.

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The article presents the proceedings of two workshops: Cloud-based Smart Technologies for Open Education Workshop (CSTOE 2022) and Illia O. Teplytskyi Workshop on Computer Simulation in Education (CoSinE 2022) held in Kyiv, Ukraine, on December 22, 2022. The CoSinE workshop focuses on computer simulation in education, including topics such as computer simulation in STEM education, AI in education, and modeling systems in education. The CSTOE workshop deals with cloud-based learning resources, platforms, and infrastructures, with topics including personalized learning and research environment d

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Sikora, Yaroslava B., Olena Yu Usata, Oleksandr O. Mosiiuk, Dmytrii S. Verbivskyi, and Ekaterina O. Shmeltser. Approaches to the choice of tools for adaptive learning based on highlighted selection criteria. [б. в.], 2021. http://dx.doi.org/10.31812/123456789/4447.

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The article substantiates the relevance of adaptive learning of students in the modern information society, reveals the essence of such concepts as “adaptability” and “adaptive learning system”. It is determined that a necessary condition for adaptive education is the criterion of an adaptive learning environment that provides opportunities for advanced education, development of key competencies, formation of a flexible personality that is able to respond to different changes, effectively solve different problems and achieve results. The authors focus on the technical aspect of adaptive learni

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Kaleagasi, Bartu, Sean McCarthy, and Peter Beaumont. Geospatial Public Policy: Global Best Practices for Harnessing the Potential of Satellite Technologies and Applications. Inter-American Development Bank, 2022. http://dx.doi.org/10.18235/0004484.

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This publication presents six case studies of public policies that promote the development and use of geospatial technologies and applications, which can be categorized in five layers: institutions, tools, data, skills and industry. The evolution of these technologies and applications over the past decade has been driven by the understanding that where people and things are located is central to smart decision making. As a result of low-cost launch vehicles, increasing numbers of satellites in orbit, new sensor technologies, machine learning algorithms, advances in cloud computing, and the eme

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