Huizhong, Z, Fanrong, M, Gui, W, Mago, B and Puyalnithi, T (2022) Research on the Automation Integration Terminal of the Education Management Platform Based on Big Data Analysis. Advances in Data Science and Adaptive Analysis, 14 (01n02). ISSN 2424-922X
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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.
Affiliation: | Skyline University College |
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SUC Author(s): | Mago, B ORCID: https://orcid.org/0000-0003-1537-1202 |
All Author(s): | Huizhong, Z, Fanrong, M, Gui, W, Mago, B and Puyalnithi, T |
Item Type: | Article |
Uncontrolled Keywords: | Big data analysis, students, monitoring, evaluation, capture, modeling neural network |
Subjects: | B Information Technology > BD Big Data Analitics |
Divisions: | Skyline University College > School of IT |
Depositing User: | Mr Veeramani Rasu |
Date Deposited: | 23 May 2022 14:17 |
Last Modified: | 23 May 2022 14:17 |
URI: | https://research.skylineuniversity.ac.ae/id/eprint/207 |
Publisher URL: | https://doi.org/10.1142/S2424922X22500036 |
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