Study on enhancing AIoT computational thinking skills by plot image-based VR

Lai, Ying-Hsun; Chen, Shih Yeh; Lai, Chin Feng; Chang, Y. C.; Su, Yu

doi:10.1080/10494820.2019.1580750

Cited by 42 publications

(32 citation statements)

References 36 publications

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“…Web multimedia systems (e.g., YouTube and Vimeo) not only offer people a platform for sharing information, but also provide them a recreation to adjust their body and mind (Duncm 2011 ). Our society was based primarily on industry, but now it is evolving into an information-based society in which people are immersed in advanced technological environments and facing numerous complex, ill-structured, rapidly changing tasks, situations, and problems (Lai et al 2019 ; Su et al 2019a , b ; Su et al 2020 ). Therefore, traditional Web systems are not appropriate for addressing these challenges, and Web multimedia systems need to evolve with the required interactive tools to survive in a world filled with various kinds of information.…”

Section: Introductionmentioning

confidence: 99%

Applying data mining techniques to explore user behaviors and watching video patterns in converged IT environments

2021

J Ambient Intell Human Comput

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Section: Introductionmentioning

confidence: 99%

Applying data mining techniques to explore user behaviors and watching video patterns in converged IT environments

2021

J Ambient Intell Human Comput

Self Cite

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“…However, due to its diverse applications and complex environments, AIoT often results in students' lack of familiarity with the application fields, or it involves a flat design, which leads students to explore problems at insufficient depths and/or choose to ignore certain parts of the problem. For example, in smart agriculture, different environments and crops should be taken into account when considering deployment and the choice of sensors; failure to do so can lead to poor overall learning outcomes (Lai et al, 2019 ; Chen et al, 2020 ).…”

Section: Introductionmentioning

confidence: 99%

Exploring Computational Thinking Skills Training Through Augmented Reality and AIoT Learning

et al. 2021

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Given the widespread acceptance of computational thinking (CT) in educational systems around the world, primary and higher education has begun thinking about how to cultivate students' CT competences. The artificial intelligence of things (AIoT) combines artificial intelligence (AI) and the Internet of things (IoT) and involves integrating sensing technologies at the lowest level with relevant algorithms in order to solve real-world problems. Thus, it has now become a popular technological application for CT training. In this study, a novel AIoT learning with Augmented Reality (AR) technology was proposed and explored the effect of CT skills. The students used AR applications to understand AIoT applications in practice, attempted the placement of different AR sensors in actual scenarios, and further generalized and designed algorithms. Based on the results of the experimental course, we explored the influence of prior knowledge and usage intention on students' CT competence training. The results show that proposed AIoT learning can increase students' learning intention and that they had a positive impact on problem solving and comprehension with AR technology, as well as application planning and design.

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“…The data is saved into the Message Queuing Telemetry Transport server (MQTT) platform, which is connected to the No relational Structured Query Language (NoSQL) database or any other Graphics Processing Unit (GPU) platform for real-time display. Examples of these web-based clients GPU are ArcGIS, API, Google Map API, ThingSpeak and WebGL [187,[198][199][200][201].…”

Section: Overview Of Groundwater Level Iots Based Systemmentioning

confidence: 99%

Groundwater Level Resources Management Modelling: A Review

Aderemi¹,

Olwal²,

Ndambuki³

et al. 2021

Preprint

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Globally, groundwater is the largest distributed storage of freshwater that plays an important role in an ecosystem’s sustainability in addition to aiding human adaptation to both climatic change and variability. However, this resource is not unlimited and its sustainability is highly dependent on its prudent use. Thus, efficient management of groundwater resources to prevent overexploitation, scarcity and drought has become a major challenge for researchers as well as water managers. To solve these challenges, many solutions such as simulation and optimisation models have been proffered through the use of historical data. Therefore, this has made efficient data gathering essential to maintain data-driven groundwater level resource management models from the observation site. The global evolution of the Internet of Things (IoTs), has increased the nature of data gathering for the management of groundwater resources. Recently, a number of research challenges such as the lack of computational efficiency and scalability due to uncertainties from input parameters to the groundwater level resource model have been revealed in the management of groundwater level resources. In addition, efficient data-driven groundwater level resource management relies hugely on information relating to changes in groundwater resource levels as well as its availability. At the moment, the groundwater managers lack an efficient and scalable groundwater management system to gather the required data. The literature revealed that the existing methods of collecting data lack efficiency to meet computational model requirements and meet management objectives. Although the IoTs enabled automated data processing systems are in existence by transmitting the generated data from IoT enabled devices into the cloud through the Internet. However, traditional IoT Internet is not scalable and efficient enough to process the generated vast IoT data. Thus, it is necessary to have an efficient and scalable IoT system to extract valuable information in real-time for groundwater level resource management. Unlike previous surveys which solely focussed on particular groundwater issues related to simulation and optimisation management models, nonetheless, this paper seeks to highlight the current groundwater level resources management models as well as the IoT contributions.

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Study on enhancing AIoT computational thinking skills by plot image-based VR

Cited by 42 publications

References 36 publications

Applying data mining techniques to explore user behaviors and watching video patterns in converged IT environments

Applying data mining techniques to explore user behaviors and watching video patterns in converged IT environments

Exploring Computational Thinking Skills Training Through Augmented Reality and AIoT Learning

Groundwater Level Resources Management Modelling: A Review

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