Building Smart University Using Innovative Technology and Architecture

Adamkó, Attila

doi:10.1007/978-3-319-59454-5_6

Cited by 7 publications

(10 citation statements)

References 16 publications

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“…Improved efficiency benefits are created with smart technologies where smart campus provides advanced education systems using AI for information and communication to enhance efficiency [58]. There are opportunities for better experiences to improve campus efficiency [59] through internet-of-things, and community-based digital technology to enhance resource utilization in a university [60]. In addition, there is financial stability and entrepreneurial leadership [61], reduction in production costs [3], effective management of energy and other resources in a university [62,63].…”

Section: Improved Efficiencymentioning

confidence: 99%

“…Innovative ecosystem comprises two main areas of sustainability and smart campus solutions. Sustainability innovations explored campuses as urban living labs (ULL) [64] for prototyping an open architecture for sustainable evolution [59]. These included the following developments: a platform for promoting sustainable campus [65], smart green campus [66] to reduce carbon footprint using solar photovoltaic (PV) power [67], mobility for promoting decarbonization and credit trading [68], and finally, a smart waste bin [54].…”

Section: Innovation Ecosystemmentioning

confidence: 99%

“…Moreover, higher education institutions (HEIs) provide technological solutions for a dynamic ecosystem created from students and academics in a digital community [79]. The smart solutions created include sensing, adaptation, inferring, and community contentment [59]. Finally, there is connectivity with everything forming an ecosystem of robust communications network [48].…”

Section: Community Engagementmentioning

confidence: 99%

“…There is integration of new technologies focusing on enhancing the life of campus citizens [8,82]. It has smart features which improve quality and support the overall experience of its citizens, providing value-added services in developing a much better and more effective environment achieved through technological changes using smart devices through the internet [59].…”

Section: Quality Of Campus Lifementioning

confidence: 99%

See 3 more Smart Citations

The Making of Smart Campus: A Review and Conceptual Framework

et al. 2023

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Smart campus is an emerging concept enabled by digital transformation opportunities in higher education. Smart campuses are often perceived as miniature replicas of smart cities and serve as living labs for smart technology research, development, and adoption, along with their traditional teaching, learning and research functions. There is currently a limited understanding of how the smart campus is conceptualized and practiced. This paper addresses this gap by using a systematic literature review following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) approach. The study uses four major domains of the smart campus, i.e., society, economy, environment, and governance, to classify existing research. These domains are each aligned to the central smart campus concepts of digital technology and big data. The analysis found little evidence of a comprehensive real-world application of the smart campus towards addressing all four domains. This highlights the infancy of the current conceptualization and practice. The findings contribute to the development of a new conceptual foundation and research directions for the smart campus notion and informs its practice through a conceptual framework. The findings reported in this paper offer a firm basis for comprehensive smart campus conceptualization, and also provide directions for future research and development of smart campuses.

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Section: Improved Efficiencymentioning

confidence: 99%

Section: Innovation Ecosystemmentioning

confidence: 99%

Section: Community Engagementmentioning

confidence: 99%

Section: Quality Of Campus Lifementioning

confidence: 99%

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The Making of Smart Campus: A Review and Conceptual Framework

et al. 2023

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“…In a newly created area as it is Smart Education, there are few papers which discuss research opportunities. One of the key topics that should be taken into account is the technological changes [54,55], and how these affect students and teachers. Other factors to take into account are the external factors [4] which may condition the research on Smart Education.…”

Section: Research Opportunitiesmentioning

confidence: 99%

Smart Education: A Review and Future Research Directions

Martín

Alario-Hoyos

Kloos

2019

13th International Conference on Ubiquitous Computing and Ambient ‪Intelligence UCAmI 2019‬

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Research and development often move forward based on buzzwords. New terms are coined to summarize new developments, often with several interpretations and without a formal definition. The term Smart Education has been coined to represent a move forward in technology-enhanced education, but what is behind it? Does it represent something essentially different from the educational technologies used before? In this paper, we do a systematic literature review to understand how this term is used, what the technologies behind it are, and what promises are made. We conclude that although the term is fuzzy, there are indeed several developments available today that can make educational technologies much more adapted to the learner and therefore underpin the learning in a smarter way.

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Examining IoT-Based Smart Campus Adoption Model: An Investigation Using Two-Stage Analysis Comprising Structural Equation Modelling and Artificial Neural Network

Sneesl,

Jusoh,

Jabar

et al. 2023

IEEE Access

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The progress and evolution of technology have been rapidly transforming various aspects of our society and daily lives, including colleges and campuses into smarter environments compared to the past. Despite the numerous advantages offered by cutting-edge technologies, such as IoT-based smart campuses, academic research on their implementation suffers from a significant lack of comprehensive information necessary to deliver efficient smart campus solutions. Therefore, the focus of this study is to investigate the significance of IoT-based smart campus adoption from 14 proposed hypotheses. The researchers collected data from stakeholders affiliated with universities in Iraq, resulting in a dataset of 442 observations. To analyze this data, a two-stage approach was employed, consisting of structural equation modeling (SEM) and reevaluated with the artificial neural networks (ANN) method. The findings provide evidence supporting the significance of various constructs. In particular, the model demonstrates satisfactory predictive relevance, indicating its effectiveness in making accurate predictions or forecasts. The ANN analysis suggests that the model has predictive capabilities. Moreover, the study findings support the importance of perceived usefulness in technology-specific factors, facilitating conditions, and propagation in organizational-specific factors, government support, social influence, and external pressure in environmental-specific factors, as well as privacy concerns, self-efficacy, satisfaction, and domain-specific knowledge in end-user-specific factors. Four hypotheses related to perceived ease of use, service collaboration, habit, and innovativeness were rejected. Notably, the study identifies propagation as the most significant predictor in the ANN analysis. The conclusions of this study can be beneficial for university administrators, manufacturers, and policymakers in understanding the essential components of smart campuses to enhance the adoption and maximize the effectiveness of smart solutions.

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Building Smart University Using Innovative Technology and Architecture

Cited by 7 publications

References 16 publications

The Making of Smart Campus: A Review and Conceptual Framework

The Making of Smart Campus: A Review and Conceptual Framework

Smart Education: A Review and Future Research Directions

Examining IoT-Based Smart Campus Adoption Model: An Investigation Using Two-Stage Analysis Comprising Structural Equation Modelling and Artificial Neural Network

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