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Bimonthly Since 1986 |
ISSN 1004-9037
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Publication Details |
Edited by: Editorial Board of Journal of Data Acquisition and Processing
P.O. Box 2704, Beijing 100190, P.R. China
Sponsored by: Institute of Computing Technology, CAS & China Computer Federation
Undertaken by: Institute of Computing Technology, CAS
Published by: SCIENCE PRESS, BEIJING, CHINA
Distributed by:
China: All Local Post Offices
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02 June 2023, Volume 38 Issue 3
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Abstract
The primary goal of any educational institution is to provide students with the best possible education and skills. To achieve this objective, it is necessary to identify students who require additional assistance and take effective steps to improve their academic performance. To produce optimal outcomes and reduce the risk of failure, educational programs need innovative methods of enhancing school efficiency. The Educational Decision Support System (EDSS) has recently gained popularity in the education system, as it enables continuous monitoring and assessment of student outcomes. However, inadequate information systems face difficulties and obstacles in utilizing EDSS to its full potential due to imprecise data, insufficient characterization, and inadequate databases. Therefore, a comprehensive literature review and selection of the most accurate predictive methodology are critical to improve the prediction process. In this study, machine learning methods were employed to construct a classifier that can predict students' success in the economic field. This paper proposes a knowledge base DSS model that utilizes machine learning techniques to evaluate student performance based on their mid-term and final-term exam results.
Keyword
DSS, Artificial Neural Network, SVM, ML, Regression
PDF Download (click here)
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