6G Wireless Communication Networks

The rise of the Internet of Things (IoT) represents a burgeoning frontier in educational innovation, particularly in the realm of e-learning. Despite its expanding capabilities, the integration of IoT technologies into e-learning within higher educational institutions (HEIs) faces numerous challenges and constraints. Limited research has delved into the implementation of IoT in e-learning, especially in developing nations. Consequently, this study seeks to explore the determinants influencing the adoption of IoT for e-learning in HEIs. The aim of the innovative ICT classroom is to assist students in acquiring new skills, adapting to new situations, and utilizing technology in a manner that leads to enhanced learning outcomes and extensive knowledge. Our suggested e-learning system, integrated with IoT technology, gauges student engagement and attentiveness throughout online lectures. The proposed technology integrates electroencephalography (EEG) with IoT. The IoT is a collection of technologies that allow embedded real-world objects to connect to the Internet and is widely used by humans to support a variety of activities. IoT should be taught using developmental learning approaches and included in higher education teaching projects. EEG devices are used to track the psychological movements of students taking online courses. Our findings demonstrate how the proposed method can be used to detect the user’s level of concentration and the required level of educational strategy. A collection of informational indexes for student attention. By using a BiLSTM network to predict undergraduate recall and temporary memory, we can accurately predict undergraduate preferred learning styles. The findings of the research demonstrate that IoT systems and devices play a pivotal role in enhancing access to education. They facilitate easier teaching and learning processes while democratizing education. When employed as educational tools, IoT devices contribute significantly to creating highly engaging learning experiences for students.

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6G Wireless Communication Networks

Cited by 5 publications

References 84 publications

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Secured energy optimization of wireless sensor nodes on edge computing platform using hybrid data aggregation scheme and Q-based reinforcement learning technique

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Internet of things enabled E-learning system for academic achievement among university students

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