Design and Application of Education Management Decision Support System Based on Big Data and Natural Language Processing
DOI:
https://doi.org/10.6911/WSRJ.202411_10(11).0003Keywords:
Big Data; Natural Language Processing; Decision Support Systems; Curriculum Resource Coverage.Abstract
The rapid development of information technology promotes the transformation of education field to data-driven. In order to improve the quality and efficiency of educational administration, this paper proposes an educational administration decision support system based on big data and natural language processing by using advanced data analysis technology. Specifically, this paper first pays attention to the importance of data collection and preprocessing to ensure the quality and consistency of data. Then, the application of natural language processing technology in educational data analysis is discussed, including emotion analysis, semantic role analysis and text conversion. The system design uses distributed file system and database technology to ensure high data availability and high system performance. The experimental results show that the system has reached the high performance standard in response speed and stability, and the highest user satisfaction is 9.9, which shows that teachers highly recognize the system. After the system is put into operation, the employment rate of students and the coverage rate of curriculum resources have been significantly improved (99.8% and 97.3% respectively), which shows that the system has great potential to improve the allocation of educational resources and improve the utilization rate of resources.
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[1] Zhou Zhimin. Research on Logistics Management Decision Support System Based on Artificial Intelligence [J]. Wireless Internet Technology, 2023,20 (15): 45-47+51.
[2] An Jianliang. Analysis of Educational Decision Support System Based on Data Collection and Digital Technology [J]. Integrated Circuit Application, 2023,40 (8): 206-207.
[3] He Huan, Li Li, He Cuihuan, Fang Lingling, Ma Li, Liu Awei, Li Congyang. The application of clinical decision support systems in the preparation for discharge of patients undergoing knee arthroscopic meniscus surgery [J]. Anhui Medical Journal, 2022, 43 (9): 1092-1095.
[4] Zhao Yanhua, Zhang Jiachen, Ma Changxiao. Design and practice of a comprehensive management support system for hypertension based on national guidelines [J]. Chinese Journal of Health Information Management, 2022, 19 (3): 365-371.
[5] Zhu Anqing, Hao Dejing, Chen Ting. Research on Decision Support Systems for Shipyard Planning Management [J]. Journal of Jiangsu University of Science and Technology (Natural Science Edition), 2021,35 (6): 78-83.
[6] Gupta S, Modgil S, Bhattacharyya S, et al. Artificial intelligence for decision support systems in the field of operations research: review and future scope of research[J]. Annals of Operations Research, 2022, 308(1): 215-274.
[7] Rani P, Kumar R, Ahmed N M O S, et al. A decision support system for heart disease prediction based upon machine learning[J]. Journal of Reliable Intelligent Environments, 2021, 7(3): 263-275.
[8] Pagano A, Giordano R, Vurro M. A decision support system based on AHP for ranking strategies to manage emergencies on drinking water supply systems[J]. Water Resources Management, 2021, 35(2): 613-628.
[9] Fox N, Campbell-Arvai V, Lindquist M, et al. Gamifying decision support systems to promote inclusive and engaged urban resilience planning[J]. Urban Planning, 2022, 7(2): 239-252.
[10] Althubiti S, Escorcia-Gutierrez J, Gamarra M, et al. Improved metaheuristics with machine learning enabled medical decision support system[J]. Computers, Materials and Continua, 2022, 73(2): 2423-2439.
[11] Khurana D, Koli A, Khatter K, et al. Natural language processing: state of the art, current trends and challenges[J]. Multimedia tools and applications, 2023, 82(3): 3713-3744.
[12] Guellil I, Saâdane H, Azouaou F, et al. Arabic natural language processing: An overview[J]. Journal of King Saud University-Computer and Information Sciences, 2021, 33(5): 497-507.
[13] Tsujii J. Natural language processing and computational linguistics[J]. Computational Linguistics, 2021, 47(4): 707-727.
[14] Maulud D H, Zeebaree S R M, Jacksi K, et al. State of art for semantic analysis of natural language processing[J]. Qubahan academic journal, 2021, 1(2): 21-28.
[15] Min B, Ross H, Sulem E, et al. Recent advances in natural language processing via large pre-trained language models: A survey[J]. ACM Computing Surveys, 2023, 56(2): 1-40.
[16] Gu Y, Tinn R, Cheng H, et al. Domain-specific language model pretraining for biomedical natural language processing[J]. ACM Transactions on Computing for Healthcare (HEALTH), 2021, 3(1): 1-23.
[17] Zhang T, Schoene A M, Ji S, et al. Natural language processing applied to mental illness detection: a narrative review[J]. NPJ digital medicine, 2022, 5(1): 1-13.
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