Education is one of the areas with a higher impact on digitalization, which takes various forms such as education through digital devices and technology to improve the learning process. Online and tangible computing platforms have become more interested in pursuing active teaching through educational technologies within the curriculum. Some common problems and considerations can be addressed, such as access, capacity, financing, periodic progress measurements, and evaluation results. The productive learning activity can be immediately and constantly monitored by proposing Digital Tangible Intelligent Monitoring systems (DTIMS). The decision tree method’s teaching methodology can efficiently perform this proposed system, which monitors the former challenges. At the same time, the next is resolved by a dynamic evaluation process based on the Internet of Things (IoT). The research is evaluated using the education systems currently adopted. The results highlighted the potential of the proposed model and helped to gain information in digital teaching. The simulation analysis is performed based on accuracy 97.69%, vulnerability 91.09%, and efficiency, proving the proposed framework’s reliability of 85.10%.
Education is a dynamic system by which students perceive the factors necessary to fit them into the society. Education is mainly intentional learning that grooms individuals to achieve success in their adult lives. Evaluation of teaching techniques, course management (CM), communication, and student monitoring are the main characteristics of today’s education system. The aim to plan the curriculum of education management in both schools and colleges leads to the implementation of an MS-BDA. The development process for evaluation of teaching techniques and CM includes the use of the sentiment analysis method, which assesses the emotional feelings of students studying the course by managing curriculum quality. The big data analysis with MNN is developed by considering the communication and student monitoring system. This system evaluates the monitoring model provided in MS-BDA for assessing student communication on merging the voice-over with the communication language processing system. The simulation analysis is performed based on accessibility, adaptability, and efficiency, proving the proposed framework’s reliability. Therefore, the system outputs an accuracy of 99.1% when compared to the existing methods.
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