A Context-Aware IoT and Deep-Learning-Based Smart Classroom for Controlling Demand and Supply of Power Load

Paudel, Prabesh; Kim, Sang-Kyoon; Park, Soonyoung; Choi, Kyoung-Ho

doi:10.3390/electronics9061039

Cited by 16 publications

(6 citation statements)

References 42 publications

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“…This model consisted of four parts: GBL objectives, GBL process, GBL evaluation and smart classrooms. The model explains the process and main activities of GBL from the three stages of before the class ( Becker, 2007 ; Huang et al, 2019 ), in the class ( Garris et al, 2002 ; Uzelac et al, 2015 ; Denham et al, 2016 ; Paudel et al, 2020 ; Kim et al, 2021 ) and after the class ( Bayirtepe and Tuzun, 2007 ; Suo et al, 2008 ; Yang and Huang, 2015 ; Aguilar et al, 2020 ).…”

Section: Discussion Conclusion and Future Researchmentioning

confidence: 99%

How to Implement Game-Based Learning in a Smart Classroom? A Model Based on a Systematic Literature Review and Delphi Method

Liu-xia

Tlili

et al. 2021

Front. Psychol.

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Game-based learning (GBL) can allow learners to acquire and construct knowledge in a fun and focused learning atmosphere. A systematic literature review of 42 papers from 2010 to 2020 in this study showed that the current difficulties in implementing GBL in classrooms could be classified into the following categories: infrastructure, resources, theoretical guidance, teacher’s capabilities and acceptance of GBL. In order to solve the above problems, the study constructs a technology enhanced GBL model, from the four parts of learning objective, learning process, learning evaluation, and smart classroom. In addition, this study adopted the Delphi method, inviting a total of 29 scholars, experts, teachers and school managers to explore how to implement GBL in smart classrooms. Finally, the technology enhanced GBL model was validated and the utilization approaches were provided at the conclusion part.

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Section: Discussion Conclusion and Future Researchmentioning

confidence: 99%

How to Implement Game-Based Learning in a Smart Classroom? A Model Based on a Systematic Literature Review and Delphi Method

Liu-xia

Tlili

et al. 2021

Front. Psychol.

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“…Not surprisingly, in the context of monitoring, various applications have been identified, e.g., health [ 60 , 89 , 115 , 158 , 159 ], smart buildings [ 90 , 116 , 160 ], agriculture [ 161 , 162 ], stress [ 61 , 117 , 136 , 163 ], transportation [ 91 ], military defense [ 164 ], etc. Other challenges comparatively highly studied in the included papers are QoS [ 92 , 93 , 118 , 128 , 137 , 138 , 139 , 152 , 165 , 166 , 167 , 168 , 169 , 170 , 171 , 172 , 173 , 174 ] with

, and energy saving [ 62 , 63 , 94 , 95 , 96 , 114 , 119 , 142 , 143 , 144 , 175 ] with

.…”

Section: Results Analysismentioning

confidence: 99%

Context-Aware Edge-Based AI Models for Wireless Sensor Networks—An Overview

Al-Saedi

Boeva

Casalicchio

et al. 2022

Sensors

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Recent advances in sensor technology are expected to lead to a greater use of wireless sensor networks (WSNs) in industry, logistics, healthcare, etc. On the other hand, advances in artificial intelligence (AI), machine learning (ML), and deep learning (DL) are becoming dominant solutions for processing large amounts of data from edge-synthesized heterogeneous sensors and drawing accurate conclusions with better understanding of the situation. Integration of the two areas WSN and AI has resulted in more accurate measurements, context-aware analysis and prediction useful for smart sensing applications. In this paper, a comprehensive overview of the latest developments in context-aware intelligent systems using sensor technology is provided. In addition, it also discusses the areas in which they are used, related challenges, motivations for adopting AI solutions, focusing on edge computing, i.e., sensor and AI techniques, along with analysis of existing research gaps. Another contribution of this study is the use of a semantic-aware approach to extract survey-relevant subjects. The latter specifically identifies eleven main research topics supported by the articles included in the work. These are analyzed from various angles to answer five main research questions. Finally, potential future research directions are also discussed.

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“…Additionally, the voice speech model does not account for ambient noise in the classroom, and it requires more effort to use compared to a traditional switch. Other than that, the paper [15] proposes a self-controlled energy management system using deep learning. It can recognize student activity and predict future ambiance conditions based on current sensor data.…”

Section: A Related Work A) Indoor Environment Monitoring and Manipula...mentioning

confidence: 99%

Classroom Environment Analysis Via Internet of Things

Tan,

Ng,

Ramasamy

2024

JIWE

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In this era of rapid technological advancement, the potential of the digital age has opened up numerous possibilities for our society. However, despite these advancements, traditional classrooms still lack the necessary technology to create an optimal learning environment for students. Consequently, students may struggle to effectively acquire knowledge within classrooms. This paper aims to conduct a classroom environment analysis using Internet of Things technology to gather data and uncover valuable insights. The proposed solution involves an embedded system for controlling and monitoring the classroom environment, as well as exporting historical data for further research. By ensuring accurate data collection, this paper seeks to facilitate meaningful improvements in the classroom environment, aligning with the principle of "garbage in, garbage out" in computer science.

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A Context-Aware IoT and Deep-Learning-Based Smart Classroom for Controlling Demand and Supply of Power Load

Cited by 16 publications

References 42 publications

How to Implement Game-Based Learning in a Smart Classroom? A Model Based on a Systematic Literature Review and Delphi Method

How to Implement Game-Based Learning in a Smart Classroom? A Model Based on a Systematic Literature Review and Delphi Method

Context-Aware Edge-Based AI Models for Wireless Sensor Networks—An Overview

Classroom Environment Analysis Via Internet of Things

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