{"id":11171,"date":"2026-08-26T21:38:38","date_gmt":"2026-08-26T13:38:38","guid":{"rendered":"\/jase\/?post_type=tkuisotope&p=11171"},"modified":"2026-08-26T23:08:06","modified_gmt":"2026-08-26T15:08:06","slug":"jase-202612-35-007","status":"publish","type":"tkuisotope","link":"\/jase\/?tkuisotope=jase-202612-35-007","title":{"rendered":"Leveraging Cloud Computing and Big Data Analytics to Enhance Ideological Education Platforms in Universities"},"content":{"rendered":"\n
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<\/i> 2026<\/a><\/p>\n<\/div>\n\n\n\n

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<\/i> Volume 35<\/a><\/p>\n<\/div>\n\n\n\n

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Weijian Shen, Bingjun Yang, Le Yang, and Chaobiao Meng<\/i><\/a><\/p>\n\n\n\n

School of Hydraulic Engineering, Zhejiang University of Water Resources and Electric Power, Hangzhou 310018, Zhejiang, China<\/p>\n<\/div>\n\n\n\n

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Received: May 22, 2026
Accepted: July 24, 2026
Publication Date: August 26, 2026<\/p>\n<\/div>\n<\/div>\n\n\n\n

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The Overall Proposed Work<\/p>\n<\/div>\n<\/div>\n\n\n\n

<\/i> Copyright <\/strong>The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0)<\/a>, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.<\/p>\n\n\n\n

Download Citation: BibTeX <\/a>| http:\/\/dx.doi.org\/10.6180\/jase.202612_35.007<\/a> <\/p>\n\n\n\n

Download PDF<\/a><\/p>\n\n\n\n

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Education is going through a remarkable transformation brought about by the convergence of cloud computing and big data, which have enabled educational institutions to develop very different and individualized learning experiences for each student depending on his\/her needs. But, even with the technology in place, the tracking of engagement in ideological education has not been able to break its limitations, causing disengagement, which is consequently leading to slow overall learning. The authors of the paper propose to create a flexible and tolerant cloud-based educational platform using big data analytics powered by AI in the Higher Education sector. One of the main goals of the research is to establish a system that will employ AI for the real-time classification of student engagement based on EEG signals and behavioral data and will thus adapt the content and the learning paths accordingly. The investigation will utilize a combination of Multilayer Perceptron (MLP) and Long Short-Term Memory networks (LSTM) African Vultures Optimization Algorithm (AVOA) will be used for feature selection, and cloud-based data storage will be provided to make the system scalable. The performance results of the proposed system have shown a very significant improvement over traditional models, with the MLP-LSTM combination achieving an accuracy of 92.6%, precision of 92.1%, recall of 91.8%, and F1 score of 91.9%.<\/p>\n\n\n\n

Keywords: EEG Analysis, Student Engagement, Feature Optimization, AVOA, Cognitive Monitoring, Intelligent Systems<\/em><\/p>\n\n\n\n

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