Development of an Intelligent System for Analyzing the Achievements of a University Student
- Authors: Mikhaylova S.S.1
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Affiliations:
- Financial University under the Government of the Russian Federation
- Issue: Vol 20, No 2 (2024)
- Pages: 185-192
- Section: Mathematical, Statistical and Instrumental Methods in Economics
- URL: https://journals.eco-vector.com/2541-8025/article/view/633815
- DOI: https://doi.org/10.33693/2541-8025-2024-20-2-185-192
- EDN: https://elibrary.ru/OBBSPK
- ID: 633815
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Abstract
Education in the modern world is an integral part of the formation of a personality, therefore, it is given special attention. Digital development in the field of higher education requires automation of many university processes, and in order to improve the quality of training specialists and ensure the objectivity of evaluating their achievements, universities are introducing a rating system. The main objectives of such systems are to increase the motivation of students and encourage them to work independently. This paper presents a rating system based on such aspects of student activity as educational, scientific, social, cultural, creative, and sports. The paper uses intellectual analysis of student achievements using the taxonomy of the subject area and machine learning methods. An intelligent system for analyzing student achievements has been developed.
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About the authors
Svetlana S. Mikhaylova
Financial University under the Government of the Russian Federation
Author for correspondence.
Email: ssmihailova@mail.ru
ORCID iD: 0000-0001-9183-8519
Dr. Sci. (Econ.), Professor, Department of Data Analysis and Machine Learning, Faculty of Information Technology and Big Data Analysis
Russian Federation, MoscowReferences
- Bruno Cristos Madeira, Tugrul Tasci, Numan Celebi. Prediction of Student Performance Using Rough Set Theory and Backpropagation Neural Networks // European Scientific Journal, ESJ. February 2021 edition Vol.17, No.7. URL: https://eujournal.org/index.php/esj/article/view/14028.
- Kongsakun K. Neural Network Modeling for an Intelligent Recommendation System Supporting SRM for Universities in Thailand // WSEAS Transaction on Computers. —2012. —Vol. 11. —No. 1.
- Matthew H. The Influence of Teaching Methods on Students Achievement on Virginia's End of Course Standards of Learning Test for Algebra-I: dissertation for the degree of Doctor of Philosophy. —USA, Virginia, Blacksburg: Virginia Polytechnic University, 2002.
- Mohammed Abdullah Al-Hagery, Maryam Abdullah Alzaid, Tahani Soud Alharbi and Moody Abdulrahman Alhanaya. Data Mining Methods for Detecting the Most Significant Factors Affecting Students’ Performance. // Information Technology and Computer Science, 2020, 5, 1-13. URL: http://www.mecs-press.org/ijitcs/ijitcs-v12-n5/IJITCS-V12-N5-1.pdf.
- Oladokun, V.O Adebanjo, A.T. and Charles-Owaba, O.E. (2008). Predicting Students Academic Performance using Artificial Neural Network: A case study of an Engineering Course. Pacific Journal of Science and Technology, Vol.9(1):72-79.
- Sadiq Husssain, Silvia Gaftandzhieva, Md. Maniruzzaman, Rositsa Doneva, Zahraa Fadhil Muhsin. Regression analysis of student academic performance using deep learning // Education and Information Technologies (2021) 26:783–798. URL: https://doi.org/10.1007/s10639-020-10241-0.
- Shoukat H., Fahad M., Hamid K. and Awais A. Factors Contributing to Students Academic Performance: A Case Study of Islamic University Sub-Campus // American Journal of Education research. —2013. —Vol. 1. —No. 8. —pp. 283–289.
- Budaev E.S., Smolina L.S. Research in the field of intellectual analysis of students' achievements at the university. In the collection: Collection of articles of the national scientific and practical conference. —Ulan-Ude. Education and Science, 2020. pp. 285–291.
- Methodology for calculating the individual student rating of the Northwestern Institute of Management of the RANEPA. [electronic resource]. —Access mode: https://sziu.ranepa.ru/images/news_doc/2019/metodika_rascheta_reytinga_2019.pdf (date of request: 12/24/2023).
- The regulation on the point-rating system for evaluating the achievements of students of Novosibirsk State Technical University. [electronic resource]. —Access mode: https://www.nstu.ru/static_files/38655/file/NSTU_Polozenie%20o%20BRS.pdf (date of access: 12/24/2023).
- Regulations on the point-rating system of achievements of students of the SibGUTI Federal State Educational Educational Institution. [electronic resource]. —Access mode: https://ita.sibsutis.ru/sites/csc.sibsutis.ru/files/files/Point_rating_system.pdf (date of access: 12/24/2023).
- Regulations on the rating system for the comprehensive assessment of students' knowledge of higher education educational programs—bachelor's degree programs, specialty programs and master's degree programs of the National Research University Higher School of Economics. [electronic resource]. —Access mode: https://www.hse.ru/data/xf/644/439/1477/Положение%20о%20рейтинговой%20системе%20комплекс..овательных%20программ%20высшего%20об.docx (date of access: 12/24/2023).
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