Self-Organized Laboratories for Smart Campus

Celdrán, Alberto Huertas; Clemente, Félix J. García; Sáenz, Jacobo; Torre, Luís de la; Salzmann, Christophe; Gillet, Denis

doi:10.1109/tlt.2019.2940571

Cited by 26 publications

(16 citation statements)

References 26 publications

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“…Intelligent terminal equipment can realize real-time collection, calculation, and analysis of public physical education field information, which is an important research theme and basis, in the process of monitoring the quality of public physical education in schools. At the same time, with the wide application of intelligent motion detector and other equipment, various information monitoring of public physical education teaching in smart campus, as well as the real-time interaction between students and sports equipment, teachers and environment have become a reality [18]. Monitoring the use of intelligent devices has become an important prerequisite for the quality of public PE reform.…”

Section: Making Full Use Of Intelligent Terminal To Monitormentioning

confidence: 99%

Construction of the Teaching Quality Monitoring System of Physical Education Courses in Colleges and Universities Based on the Construction of Smart Campus with Artificial Intelligence

Huang

Wang

2021

Mathematical Problems in Engineering

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With regard to the development of colleges and universities, ensuring the quality of education is the fundamental goal and main task of teaching daily management. With the continuous improvement of the application level of the Internet and other information technologies, the construction of smart campus in colleges and universities in China is rapidly advancing. This paper studies the construction and innovation strategy of the public sports quality monitoring system and discusses the changes in college students’ sports quality after the introduction of smart campuses from the perspective of artificial intelligence and the creation of smart universities. In this paper, the field survey method and other research methods are combined to study, and in the process of data storage, SQL Server database platform is used to store the data. This study shows that the proportion of each element of physical state management has changed significantly before and after college entrance. According to the data, since the introduction of smart campus real name system identification, tracking data, and evaluation functions, the number of college students’ physical exercise has increased significantly. The number of students with exercise plan in school 1 has increased from 70 to 222, and that of school 2 has increased from 49 to 199. Before the introduction, the students were very satisfied with the learning effect of physical education, which was 40.12% and increased to 45.70% after the introduction. Before the introduction, the students were very satisfied with the sports equipment, which was 30.12% before the introduction and increased to 35.24% after the introduction. Therefore, building a system for monitoring the quality of public sports in universities is very important for improving the quality of education in public sports in universities and plays an active role in promoting the physical and mental health of students.

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Section: Making Full Use Of Intelligent Terminal To Monitormentioning

confidence: 99%

Construction of the Teaching Quality Monitoring System of Physical Education Courses in Colleges and Universities Based on the Construction of Smart Campus with Artificial Intelligence

Huang

Wang

2021

Mathematical Problems in Engineering

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“…The presented architecture could be of great value also for the remote lab community. While virtualization techniques had been already explored [27][28][29], this architecture could increase the flexibility of remote labs, by supporting the configuration and deployment of remote experiments [32]. Moreover, it supports the collection of multimodal data (coming both from hardware and software) necessary to support the smart adaptation to the learning process.…”

Section: Discussionmentioning

confidence: 99%

“…Students had access to VMs for a given time and, once finished, a snapshot was made before restoring and preparing the VMs for new students. In [28], the authors proposed a solution that considered virtualization techniques to adapt the resources of remote laboratories at anytime and on-demand. Several experiments demonstrated how the usage of computing resources was optimized to guarantee the smart labs quality of service.…”

Section: Remote Classrooms and Labsmentioning

confidence: 99%

A Scalable Architecture for the Dynamic Deployment of Multimodal Learning Analytics Applications in Smart Classrooms

Celdrán

Ruipérez‐Valiente

Clemente

et al. 2020

Sensors

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The smart classrooms of the future will use different software, devices and wearables as an integral part of the learning process. These educational applications generate a large amount of data from different sources. The area of Multimodal Learning Analytics (MMLA) explores the affordances of processing these heterogeneous data to understand and improve both learning and the context where it occurs. However, a review of different MMLA studies highlighted that ad-hoc and rigid architectures cannot be scaled up to real contexts. In this work, we propose a novel MMLA architecture that builds on software-defined networks and network function virtualization principles. We exemplify how this architecture can solve some of the detected challenges to deploy, dismantle and reconfigure the MMLA applications in a scalable way. Additionally, through some experiments, we demonstrate the feasibility and performance of our architecture when different classroom devices are reconfigured with diverse learning tools. These findings and the proposed architecture can be useful for other researchers in the area of MMLA and educational technologies envisioning the future of smart classrooms. Future work should aim to deploy this architecture in real educational scenarios with MMLA applications.

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“…Students that are not physically present on the campus can undergo long waiting time in queues for remote laboratories. To improve their experience, Huertas Celdrán et al [150] developed autonomic capabilities to adapt remote laboratories configuration according to the end-user demand and dynamically change the lab service under specific circumstances. The proposed architecture is based on network function virtualization (NFV) and software-defined networking (SDN).…”

Section: G Smart Classrooms and Conference Roomsmentioning

confidence: 99%

Smart Campuses: Extensive Review of the Last Decade of Research and Current Challenges

et al. 2021

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Novel intelligent systems to assist the energy transition and improve sustainability and people's life can be deployed at different scales, ranging from a house to an entire region. University campuses are an interesting intermediate size (big enough to matter and small enough to be tractable) for research, development, test and training on the integration of smartness at all levels, which led to the emergence of the concept of "smart campus" over the last few years. This review article proposes an extensive analysis of the scientific literature on smart campuses from the last decade (2010-2020). The 182 selected publications are distributed into seven categories of smartness: smart building, smart environment, smart mobility, smart living, smart people, smart governance and smart data. The main open questions and challenges regarding smart campuses are presented at the end of the review and deal with sustainability and energy transition, acceptability and ethics, learning models, open data policies and interoperability. The present work was carried out within the framework of the Energy Network of the Regional Leaders Summit (RLS-Energy) as part of its multilateral research efforts on smart regions.

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Self-Organized Laboratories for Smart Campus

Cited by 26 publications

References 26 publications

Construction of the Teaching Quality Monitoring System of Physical Education Courses in Colleges and Universities Based on the Construction of Smart Campus with Artificial Intelligence

Construction of the Teaching Quality Monitoring System of Physical Education Courses in Colleges and Universities Based on the Construction of Smart Campus with Artificial Intelligence

A Scalable Architecture for the Dynamic Deployment of Multimodal Learning Analytics Applications in Smart Classrooms

Smart Campuses: Extensive Review of the Last Decade of Research and Current Challenges

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