IoT enabled e-learning system for higher education
Kailash Kumar, Abdulaziz Albesher
Abstract
In a pandemic situation, students are continuing their studies in online mode. It is difficult to achieve a clear understanding of the subject and improve educational quality in an online mode. It is difficult for teachers to monitor the students in online mode. Keeping an eye on students' attentiveness during an online lesson is crucial. Each participating student is at a distinct degree of proficiency. The ultimate objective is to raise the standard of online education. Our proposed IoT-enabled e-Learning System, assesses students' levels of engagement and focus throughout online lessons, the suggested technology combines the Internet of Things (IoT) and electroencephalography (EEG). The Internet of Things is a group of technologies that enable physical embedded objects to connect to the Internet and are extensively integrated into human activities to support various activities. IoT needs to be covered in the higher education curriculum and taught using a development learning approach. The EEG device is used to monitor the student's brain activities online classes. Our results demonstrate that the suggested method can be utilized to discriminate between a user's concentration state and the required level of teaching methods. Datasets on student attention are gathered. Effectively anticipate student re-collection long-short-term memory (BiLSTM) networks can be used to effectively predict students' preferred learning modes. The prediction accuracy of the suggested approach is 97.16%. A system for e-learning that is IoT enabled enhances academic performance.