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| Title | A Novel Internet Of Things-Supported Intelligent Education Management System Implemented Via Collaboration Of Knowledge And Data |
|---|---|
| ID_Doc | 3407 |
| Authors | Wei W.; Jin Y. |
| Year | 2023 |
| Published | Mathematical Biosciences and Engineering, 20, 7 |
| DOI | http://dx.doi.org/10.3934/mbe.2023600 |
| Abstract | The past decade has witnessed great progress in the Internet of Things (IoT), which can provide integrated platforms for data with various formats and to serve different parts of human society. Although IoT-supported education management systems have achieved some successful applications, most existing systems cannot perform intelligent information processing, such as autonomous planning and optimal scheduling. To remedy this gap, this work proposes a novel IoT-supported intelligent education management system that is implemented via collaboration of knowledge and data. First, the macroscopic architecture is designed according to field knowledge of education management, and a clustering-based data analysis algorithm is utilized to visualize real-time classroom characteristics. Then, statistics of learning status are generated, and personalized following plans are accordingly suggested to different specific users. Finally, the functions of the designed smart education management system are tested via computer simulation operations. The obtained results show that the proposal can work well under a real-time data stream and is expected to serve as a typical education management application in smart cities. Through verification, it is found that the integration of general education and professional courses is the ideal starting point for the design of the elements of an optimal course structure for engineering practice majors. In particular, we should strengthen the reform of the following course types: introduction to design, general education, concentrated practice, comprehensive design and peak courses. ©2023 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0) |
| Author Keywords | data analysis; intelligent management systems; Internet of Things; knowledge discovery |
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