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Journal articles on the topic 'Smart Learning Environments'

Author: Grafiati

Published: 4 June 2021

Last updated: 26 July 2025

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1

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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Ghani, Erlane, Kamaruzzaman Muhammad, Mazurina Mohd Ali, Razana Juhaida Johari, and Citra Sukmadilaga. "Factors influencing effective smart learning environment in Malaysian universities." Journal of Asian Scientific Research 13, no. 4 (2023): 195–208. http://dx.doi.org/10.55493/5003.v13i4.4953.

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This study aims to examine the factors influencing an effective smart learning environment in the universities in Malaysia from the perspective of the students. Specifically, this study examines three key factors: students' attitudes towards smart learning, their perception of smart learning tools, and the impact of course design in the smart learning environment. This study utilized a quantitative approach using a questionnaire survey that was administered to a sample of 386 students enrolled in both private and public institutions without regard to their academic year. This study demonstrate

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Carrruana Martín, Adrián, Carlos Alario-Hoyos, and Carlos Delgado Kloos. "Smart Groups: A system to orchestrate collaboration in hybrid learning environments. A simulation study." Australasian Journal of Educational Technology 38, no. 6 (2022): 150–68. http://dx.doi.org/10.14742/ajet.6776.

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COVID-19 has brought new hybrid learning environments with some students in the classroom and some others online, synchronously, due to the needs of social distancing. These new hybrid learning environments pose new challenges, for example for group collaboration. This paper presents Smart Groups, a system aimed at helping teachers to orchestrate collaboration in hybrid learning environments and assesses its usability and usefulness through a simulation study. Smart Groups identifies the students that are in the classroom and online, automates the creation of groups (recommending collaborative

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Jiménez-Macías, Alberto, Pedro J. Muñoz-Merino, Margarita Ortiz-Rojas, Mario Muñoz-Organero, and Kloos Carlos Delgado. "Content Modeling in Smart Learning Environments: A systematic literature review." JUCS - Journal of Universal Computer Science 30, no. (3) (2024): 333–62. https://doi.org/10.3897/jucs.106023.

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Educational content has become a key element for improving the quality and effectiveness of teaching. Many studies have been conducted on user and knowledge modeling using machine-learning algorithms in smart-learning environments. However, few studies have focused on content modeling to estimate content indicators based on student interaction. This study presents a systematic literature review of content modeling using machine learning algorithms in smart learning environments. Two databases were used: Scopus and Web of Science (WoS), with studies conducted until August 2023. In addition, a m

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Thomas, Lauren Josie, Michael Parsons, and Dean Whitcombe. "Assessment in Smart Learning Environments: Psychological factors affecting perceived learning." Computers in Human Behavior 95 (June 2019): 197–207. http://dx.doi.org/10.1016/j.chb.2018.11.037.

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García-Tudela, Pedro Antonio, María Paz Prendes-Espinosa, and Isabel María Solano-Fernández. "Smart Learning Environments y ergonomía: una aproximación al estado de la cuestión." Journal of New Approaches in Educational Research 9, no. 2 (2020): 245. http://dx.doi.org/10.7821/naer.2020.7.562.

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Educational technology evolves constantly, in line with the innovative technologies we implement, but always catering for the improvement of teaching and learning. For this, Smart Learning Environments (SLE) emerge as an optimal alternative to traditional teaching as, through ergonomics, an inclusive outlook which is bound to enhance the educational experience of every student is provided. The method utilized is based on a systematic review of the existing literature which has allowed us to analyze in depth a final sample of 19 documents after an initial review of 633, being these all the work

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Lee, Taewoo, Yumin Choi, and Hyunbum Kim. "Reinforcing Deep Learning-Enabled Surveillance with Smart Sensors." Sensors 25, no. 11 (2025): 3345. https://doi.org/10.3390/s25113345.

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It is critical to solidify surveillance in 3D environments with heterogeneous sensors. This study introduces an innovative deep learning-assisted surveillance reinforcement system with smart sensors for resource-constrained cyber-physical devices and mobile elements. The proposed system incorporates deep learning technologies to address the challenges of dynamic public environments. By enhancing the adaptability and effectiveness of surveillance in environments with high human mobility, this paper aims to optimize surveillance node placement and ensure real-time system responsiveness. The inte

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Stark, Holger. "Artificial microswimmers get smart." Science Robotics 6, no. 52 (2021): eabh1977. http://dx.doi.org/10.1126/scirobotics.abh1977.

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Ramakić, Adnan, and Zlatko Bundalo. "Unlocking the Potential of Smart Environments Through Deep Learning." Computers 14, no. 8 (2025): 296. https://doi.org/10.3390/computers14080296.

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This paper looks at and describes the potential of using artificial intelligence in smart environments. Various environments such as houses and residential and commercial buildings are becoming smarter through the use of various technologies, i.e., various sensors, smart devices and elements based on artificial intelligence. These technologies are used, for example, to achieve different levels of security in environments, for personalized comfort and control and for ambient assisted living. We investigated the deep learning approach, and, in this paper, describe its use in this context. Accord

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David Netto Silveira, Pedro, Davidson Cury, and Crediné Silva de Menezes. "Um framework para concepção de Smart Learning Environments." RENOTE 18, no. 2 (2021): 21–30. http://dx.doi.org/10.22456/1679-1916.110193.

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A consequência da imersão da sociedade na cultura digital, é uma tendência em avançarmos rumo a uma escola digital apoiada pela aprendizagem informal e personalizada. Os ecossistemas de aprendizagem, se devidamente fomentados, são um potencializador desta escola em meio a esse processo de transformação. Propomos neste artigo um framework para apoiar a modelagem de ambientes de aprendizagem inteligentes capazes de estimular as interações nos diversos ecossistemas que participamos e dessa forma favorecer a construção do conhecimento. Também apresentamos três instâncias do framework, a primeira p

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20

Şerban, Camelia, and Lungu Ioan. "QLearn: Towards a framework for smart learning environments." Procedia Computer Science 176 (2020): 2812–21. http://dx.doi.org/10.1016/j.procs.2020.09.273.

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Vrachimis, Andreas, Stella Gkegka, and Kostas Kolomvatsos. "Resilient edge machine learning in smart city environments." Journal of Smart Cities and Society 2, no. 1 (2023): 3–24. http://dx.doi.org/10.3233/scs-230005.

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Distributed Machine Learning (DML) has emerged as a disruptive technology that enables the execution of Machine Learning (ML) and Deep Learning (DL) algorithms in proximity to data generation, facilitating predictive analytics services in Smart City environments. However, the real-time analysis of data generated by Smart City Edge Devices (EDs) poses significant challenges. Concept drift, where the statistical properties of data streams change over time, leads to degraded prediction performance. Moreover, the reliability of each computing node directly impacts the availability of DML systems,

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Nikolov, Roumen, Elena Shoikova, Milena Krumova, Eugenia Kovatcheva, Velian Dimitrov, and Alexander Chikalanov. "On Learning in a Smart City Environment." Serdica Journal of Computing 9, no. 3-4 (2016): 223–40. http://dx.doi.org/10.55630/sjc.2015.9.223-240.

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Advances in technology in recent years have changed the learningbehaviours of learners and reshaped teaching methods and learningenvironments. This paper overviews a foundational framework and providesmodels for planning and implementing smart learning environments. Gartner’s 2015 Hype Cycle for Emerging Technologies identifies the computinginnovations such as Internet of Things, Advanced Analytics, Machine Learning,Wearables, etc., that organisations should monitor. Learners and students, being the future drivers of these industries, are the main human resource tofulfil the vacancies of these

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23

Sayyad, Ehtesham Ali. "Smart Learning Using Generative AI." International Journal for Research in Applied Science and Engineering Technology 13, no. 4 (2025): 4249–56. https://doi.org/10.22214/ijraset.2025.69192.

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Abstract: IttalksabouthowgenerativeAIcanchangetheeducationalenvironment by shininglight onitto explain howitispossible to customizelearning experiences for students. The proposed platform enables the student to select particular topics and algorithms; with this, theAI will be in a position to create particular animated videos with Indian English captions and voiceovers, which seems to speak out an imperativemethodologyofclarificationofcomplexconceptswithincreasedstudent engagement and understanding. The service also keeps instructors updated on the learningperformanceandengagement oflearners;t

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Yassin, Abdulnassir, Salman Rashid, and Husna Juma Mlwale. "Gamification and Smart Learning: A Framework for Digital Motivation." Journal of Educational Technology Innovation and Applications 1, no. 01 (2025): 1–8. https://doi.org/10.56741/jetia.v1i01.997.

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Integrating gamification techniques into smart learning environments has gained considerable attention as a method to enhance student engagement and motivation. This article explores the connection between gamification and smart learning by proposing a conceptual framework for digital motivation. Drawing upon existing literature, the paper identifies key components of gamification and smart learning and presents a model that leverages digital tools to improve educational outcomes. The results of various studies examining the effects of gamification in digital learning environments are discusse

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O. Ewata, Thompson, and Francis Ogunbodede Kolawole. "Smart Learning Environments: A Pathway to quality University education in Nigeria." KIU Journal of Education 4, no. 2 (2024): 113–24. https://doi.org/10.59568/kjed-2024-4-2-11.

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The study examined smart learning environments as a pathway to quality university education in Nigeria. The population of the study comprised all lecturers in the four government-owned universities in Bayelsa State. However, the researchers were unable to determine the precise number of lecturers at the time of this investigation due to data collection constraints. Based on the advice of Fox et al. (2007) and Meyer (1979), the researchers chose to adapt 384 samples from a population with an unconstrained range. The data used were collected through an online questionnaire; however, 296 lecturer

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Li, Kam Cheong, and Billy Tak-Ming Wong. "Review of smart learning: Patterns and trends in research and practice." Australasian Journal of Educational Technology 37, no. 2 (2021): 189–204. http://dx.doi.org/10.14742/ajet.6617.

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This article presents a review of the literature on smart learning in order to provide a comprehensive overview of its latest developments in research and practice. The review covered 90 studies published from 2010 to 2019, which were collected from three publication databases, namely Web of Science, Scopus and ProQuest. They were analysed for the patterns and trends in terms of publication years, sources of publication, countries or regions of publication, research purposes, research methods, educational levels of the studies, application domains, research issues, research participants, learn

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H R, Seema. "Smart Shopping Cart." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 02 (2025): 1–9. https://doi.org/10.55041/ijsrem41357.

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Recently, many attempts have been made to reduce the time required for payment in various shopping environments. In addition, with advancements in artificial intelligence and IoT technologies, it has become easier to create unmanned environments for shopping, reducing the need for human intervention. This paper proposes a smart shopping cart system based on low-cost IoT equipment and deep learning object detection technology. The system consists of a camera for real-time product detection, an ultrasonic sensor as a trigger, a weight sensor to determine if a product enters or exits the shopping

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Bali, Siddheshwar. "Smart Learning Using Generative AI." International Journal for Research in Applied Science and Engineering Technology 13, no. 3 (2025): 435–42. https://doi.org/10.22214/ijraset.2025.67226.

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It talks about how generative AI can change the educational environment by shining light on it to explain how it is possible to customize learning experiences for students. The proposed platform enables the student to selectparticular topics and algorithms; with this, the AI will be in a position to create particular animated videos with Indian English captions and voiceovers, whichseems to speak out an imperative methodology of clarification of complex concepts with increased student engagement and understanding. The service also keeps instructors updated on the learning performance and engag

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B, Doraswamy, Lokesh Krishna K, and Hariprasad Tarigonda. "IoT Generated Multi-Modality Data Analysis Using a Deep Learning Framework for Managing Sustainability in Smart Environments." International Research Journal of Multidisciplinary Scope 05, no. 03 (2024): 706–20. http://dx.doi.org/10.47857/irjms.2024.v05i03.0829.

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Incorporating the Internet of Things (IoT) systems into Smart environments can reinforce stability and sustainability. IoT-deployed smart environment monitoring devices supports energy monitoring, water consumption, and other resource utilization details. Several earlier research works have focused on energy, water consumption, or other sustainable parameter monitoring that cannot retain and manage sustainability. This paper aims to maintain and manage the sustainability of smart environments by creating a Deep Learning Framework (DLF) to analyze the multimodality data generated by IoT devices

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Nurjaman, Iman. "THE MAIN COMPONENT FOR DESIGNING SMART LEARNING." Asia Proceedings of Social Sciences 4, no. 2 (2019): 85–88. http://dx.doi.org/10.31580/apss.v4i2.746.

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This paper discusses key components in designing smart learning. The concept of smart learning emphasises the importance of technological design to make learning better. In fact, many different definitions can be found in almost all the articles that emphasise multiple aspects and characteristics of smart learning published since 2014 in the journal Smart Learning Environments. This finding illustrates the main components of smart learning to improve knowledge and develop smart learning designs. Based on the results of the literature review it was found that, there are some common and crucial

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Lester, James C., Eun Y. Ha, Seung Y. Lee, Bradford W. Mott, Jonathan P. Rowe, and Jennifer L. Sabourin. "Serious Games Get Smart: Intelligent Game-Based Learning Environments." AI Magazine 34, no. 4 (2013): 31–45. http://dx.doi.org/10.1609/aimag.v34i4.2488.

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Intelligent game-based learning environments integrate commercial game technologies with AI methods from intelligent tutoring systems and intelligent narrative technologies. This article introduces the CRYSTAL ISLAND intelligent game-based learning environment, which has been under development in the authors’ laboratory for the past seven years. After presenting CRYSTAL ISLAND, the principal technical problems of intelligent game-based learning environments are discussed: narrative-centered tutorial planning, student affect recognition, student knowledge modeling, and student goal recognition.

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Kasperiuniene, Judita, and Ilona Tandzegolskiene. "Smart learning environments in a contemporary museum: a case study." Journal of Education Culture and Society 11, no. 2 (2020): 353–75. http://dx.doi.org/10.15503/jecs2020.2.353.375.

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Aim. The modern museum becomes an attractive learning place and space where the visitor, depending on age and competence, develops personal experience, and constructs the learning process based on personalized goals. The article aims to reveal how spaces in museums are exploited, in what ways visitors are involved in a narrative that connects the present and the past. Concept. The research uses a case-study method to investigate the POLIN Museum of the History of Polish Jews (Poland), Ruhr Museum (Germany), and Vienna Technical Museum (Austria). Within the smart learning environment cont

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Huh, Keun. "Key factors of SMART Learning environments for language classrooms." Indian Journal of Public Health Research & Development 9, no. 8 (2018): 646. http://dx.doi.org/10.5958/0976-5506.2018.00806.9.

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Laffey, James, Matthew Schmidt, Krista Galyen, and Janine Stichter. "Smart 3D collaborative virtual learning environments: A preliminary framework." Journal of Ambient Intelligence and Smart Environments 4, no. 1 (2012): 49–66. http://dx.doi.org/10.3233/ais-2011-0128.

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Valerio, Lorenzo, Andrea Passarella, and Marco Conti. "A communication efficient distributed learning framework for smart environments." Pervasive and Mobile Computing 41 (October 2017): 46–68. http://dx.doi.org/10.1016/j.pmcj.2017.07.014.

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Ruiz-Calleja, Adolfo, Miguel Bote-Lorenzo, Guillermo Vega-Gorgojo, et al. "The Potential of Open Data to Automatically Create Learning Resources for Smart Learning Environments." Proceedings 31, no. 1 (2019): 61. http://dx.doi.org/10.3390/proceedings2019031061.

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Smart Education requires bridging formal and informal learning experience. However, how to create contextualized learning resources that support this bridging remains a problem. In this paper, we propose to exploit the open data available in the Web to automatically create contextualized learning resources. Our preliminary results are promising, as our system creates thousands of learning resources related to formal education concepts and physical locations in the student’s local municipality. As part of our future work, we will explore how to integrate these resources into a Smart Learning En

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Alsamhi, Saeed H., Faris A. Almalki, Hatem Al-Dois, et al. "Machine Learning for Smart Environments in B5G Networks: Connectivity and QoS." Computational Intelligence and Neuroscience 2021 (September 18, 2021): 1–23. http://dx.doi.org/10.1155/2021/6805151.

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The number of Internet of Things (IoT) devices to be connected via the Internet is overgrowing. The heterogeneity and complexity of the IoT in terms of dynamism and uncertainty complicate this landscape dramatically and introduce vulnerabilities. Intelligent management of IoT is required to maintain connectivity, improve Quality of Service (QoS), and reduce energy consumption in real time within dynamic environments. Machine Learning (ML) plays a pivotal role in QoS enhancement, connectivity, and provisioning of smart applications. Therefore, this survey focuses on the use of ML for enhancing

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Kaimara, Polyxeni, Ioannis Deliyannis, Andreas Oikonomou, and Emmanuel Fokides. "Waking Up in the Morning (WUIM): A Smart Learning Environment for Students with Learning Difficulties." Technologies 9, no. 3 (2021): 50. http://dx.doi.org/10.3390/technologies9030050.

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Effectiveness, efficiency, scalability, autonomy, engagement, flexibility, adaptiveness, personalization, conversationality, reflectiveness, innovation, and self-organization are some of the fundamental features of smart environments. Smart environments are considered a good learning practice for formal and informal education; however, it is important to point out the pedagogical approaches on which they are based. Smart learning environments (SLEs) underline the flexibility of eclectic pedagogy that places students at the center of any educational process and takes into account the diversity

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Marques, Margarida M., João Ferreira-Santos, Rita Rodrigues, and Lúcia Pombo. "Mobile Augmented Reality Games Towards Smart Learning City Environments: Learning About Sustainability." Computers 14, no. 7 (2025): 267. https://doi.org/10.3390/computers14070267.

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This study explores the potential of mobile augmented reality games (MARGs) in promoting sustainability competencies within the context of a smart learning city environment. Anchored in the EduCITY project, which integrates location-based AR-enhanced games into an interactive mobile app, the research investigates how these tools support Education for Sustainable Development (ESD). Employing a mixed-methods approach, data were collected through the GreenComp-based Questionnaire (GCQuest) and anonymous gameplay logs generated by the app. Thematic analysis of 358 responses revealed four key learn

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Prakasha, G. S., Rituparna Chakraborty, S. Thirumalesha, et al. "EXPECTATIONS OF UNIVERSITY LECTURERS AND STUDENTS ON SMART LEARNING ENVIRONMENTS." Problems of Education in the 21st Century 81, no. 4 (2023): 501–16. http://dx.doi.org/10.33225/pec/23.81.501.

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Education in the 21st century has become more technology integrated, and the shift from conventional modalities of teaching to technology-assisted teaching methodologies accelerated during the pandemic years. The current study employed a qualitative research design (n=12) to explore this shift and the expectations of these novel teaching modalities from both facilitator and learner perspectives in urban higher education settings in India. An inductive thematic analysis of the qualitative data revealed that there is a significant gap in lecturers’ and students’ definitions and expectations of s

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Valente, Renato, Carlos Senna, Pedro Rito, and Susana Sargento. "Embedded Federated Learning for VANET Environments." Applied Sciences 13, no. 4 (2023): 2329. http://dx.doi.org/10.3390/app13042329.

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In the scope of smart cities, the sensors scattered throughout the city generate information that supplies intelligence mechanisms to learn the city’s mobility patterns. These patterns are used in machine learning (ML) applications, such as traffic estimation, that allow for improvement in the quality of experience in the city. Owing to the Internet-of-Things (IoT) evolution, the city’s monitoring points are always growing, and the transmission of the mass of data generated from edge devices to the cloud, required by centralized ML solutions, brings great challenges in terms of communication,

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Shi, Cong, Jian Liu, Hongbo Liu, and Yingying Chen. "WiFi-Enabled User Authentication through Deep Learning in Daily Activities." ACM Transactions on Internet of Things 2, no. 2 (2021): 1–25. http://dx.doi.org/10.1145/3448738.

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User authentication is a critical process in both corporate and home environments due to the ever-growing security and privacy concerns. With the advancement of smart cities and home environments, the concept of user authentication is evolved with a broader implication by not only preventing unauthorized users from accessing confidential information but also providing the opportunities for customized services corresponding to a specific user. Traditional approaches of user authentication either require specialized device installation or inconvenient wearable sensor attachment. This article sup

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Purba, Siska Wati Dewi, Han-Chieh Chao, Wu-Yuin Hwang, and Yong-Qi Tang. "Empowering vocational students: Exploring mobile learning for sustainable high-level cognition in authentic contexts." Eurasia Journal of Mathematics, Science and Technology Education 20, no. 8 (2024): em2491. http://dx.doi.org/10.29333/ejmste/14917.

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Early studies show that learning with mobile devices, also known as mobile learning, improves students’ learning in authentic contextual learning–i.e., learning connected to the real world. However, no empirical evidence has yet to firmly prove the effects of mobile technology on specific student skillsets such as learning scalability which means learning can be applied in various scenarios and learning sustainability which means learning can be sustained in real-world environments. Therefore, this study aims to explore the effect of learning using a mobile app called mobile Smart-Physics on l

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Fitri, Agus, and Uswatun Hasanah. "Smart Campus Development: Trends in Building a Digital-Based Academic Culture in Cyber Learning Environments." International Journal of Science, Technology & Management 5, no. 2 (2024): 349–57. http://dx.doi.org/10.46729/ijstm.v5i2.1078.

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This research aims to formulate the design, roadmap, implementation and impact of smart campus development in higher education in building a digital-based academic culture. This research uses qualitative field research methods with a multi-site study design and flow analysis techniques. The findings of this research show that the smart campus development strategy has a special character in higher education. Smart campus development system design requires data center infrastructure, internet, and building management through Enterprise Architecture (EA) Analysis, Jason with Token (JWT), LemonLDA

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Ionescu, Ștefan-Alexandru, Nicolae Marius Jula, Gheorghe Hurduzeu, Alexandrina Maria Păuceanu, and Alexandra-Georgiana Sima. "PRISMA on Machine Learning Techniques in Smart City Development." Applied Sciences 14, no. 16 (2024): 7378. http://dx.doi.org/10.3390/app14167378.

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This article investigates the innovative role of machine learning (ML) in the development of smart cities, emphasizing the critical interrelationship between ML and urban environments. While existing studies address ML and urban settings separately, this work uniquely examines their intersection, highlighting the transformative potential of ML in urban development. Utilizing the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) methodology, a systematic and reproducible approach was employed to review 42 relevant studies. The analysis reveals four key themes: transpor

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Lihua, Lu. "Energy-Aware Intrusion Detection Model for Internet of Vehicles Using Machine Learning Methods." Wireless Communications and Mobile Computing 2022 (May 26, 2022): 1–8. http://dx.doi.org/10.1155/2022/9865549.

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With increasing development of Internet of Things (IoT) technology, wireless communications, big data, and smart applications, vehicular communications have become ubiquitous in smart cities, smart transportation systems, and Internet of Vehicles (IoV) environments. In this paper, a new Energy-aware Intrusion Detection System (EIDS) based on intelligent two-phase contract management model is presented for vehicle-to-vehicle (V2V) strategy in the IoV environments. In this strategy, the proposed EIDS predicts safe and energy-efficient end-to-end points for communication between existing vehicles

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Jiménez-Macías, Alberto, Pedro J. Muñoz-Merino, Margarita Ortiz-Rojas, Mario Muñoz-Organero, and Carlos Delgado Kloos. "Content Modeling in Smart Learning Environments: A systematic literature review." JUCS - Journal of Universal Computer Science 30, no. 3 (2024): 333–62. http://dx.doi.org/10.3897/jucs.106023.

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Educational content has become a key element for improving the quality and effectiveness of teaching. Many studies have been conducted on user and knowledge modeling using machine-learning algorithms in smart-learning environments. However, few studies have focused on content modeling to estimate content indicators based on student interaction. This study presents a systematic literature review of content modeling using machine learning algorithms in smart learning environments. Two databases were used: Scopus and Web of Science (WoS), with studies conducted until August 2023. In addition, a m

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Tuo, Pan, Mehmet Bicakci, Albert Ziegler, and BaoHui Zhang. "Measuring Personalized Learning in the Smart Classroom Learning Environment: Development and Validation of an Instrument." Education Sciences 15, no. 5 (2025): 620. https://doi.org/10.3390/educsci15050620.

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Smart classrooms leverage intelligent and mobile technologies to create highly interactive, student-centered environments conducive to personalized learning. However, measuring students’ personalized learning experiences in these technologically advanced spaces remains a challenge. This study addresses the gap by developing and validating a Smart Classroom Environment–Personalized Learning Scale (SCE-PL). Drawing on a comprehensive literature review, content-expert feedback, and iterative item refinement, an initial pool of 48 items was reduced to 39 and subsequently to 34 following item-level

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Preuveneers, Davy, Ilias Tsingenopoulos, and Wouter Joosen. "Resource Usage and Performance Trade-offs for Machine Learning Models in Smart Environments." Sensors 20, no. 4 (2020): 1176. http://dx.doi.org/10.3390/s20041176.

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The application of artificial intelligence enhances the ability of sensor and networking technologies to realize smart systems that sense, monitor and automatically control our everyday environments. Intelligent systems and applications often automate decisions based on the outcome of certain machine learning models. They collaborate at an ever increasing scale, ranging from smart homes and smart factories to smart cities. The best performing machine learning model, its architecture and parameters for a given task are ideally automatically determined through a hyperparameter tuning process. At

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Bacciu, Davide, Maurizio Di Rocco, Mauro Dragone, Claudio Gallicchio, Alessio Micheli, and Alessandro Saffiotti. "An ambient intelligence approach for learning in smart robotic environments." Computational Intelligence 35, no. 4 (2019): 1060–87. http://dx.doi.org/10.1111/coin.12233.

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