Systems and Means of Informatics
2023, Volume 33, Issue 1, pp 68-77
METHODS OF CLASSIFYING THE DISTANCE LEARNING SYSTEM USERS IN THE MODEL OF CONSTRUCTING THEIR PERS ONALIZED LEARNING STRATEGIES
- Ya. G. Martyushova
- T. A. Mineyeva
- A. V. Naumov
Abstract
The article examines the problem of adaptation of the distance learning system to the contingent of users by constructing personalized learning strategies using their previous tests results. The main part of the suggested model is classifying users by various academic progress criteria. Comparative analysis of results of applying different classifiers for this purpose is presented.
The following types of classifiers were used: Bayes classifier, logistic regression, k-nearest neighbors algorithm, decision tree, random forest, boosting, and bootstrap aggregating classifier that uses a majority vote as the voting scheme.
The article presents the results of a numerical experiment using the data on the work of MAI distance learning system CLASS.NET.
[+] References (17)
- Kuravsky, L.S., P. A. Marmalyuk, V. I. Alkhimov, and G. A. Yuryev. 2013. Novyy podkhod k postroeniyu intellektual'nykh i kompetentnostnykh testov [A new approach to constructing intellectual and competence-based tests]. Modelirovanie i analiz dannykh [Modelling and Data Analysis] 1:4-28.
- Kuravsky, L. S., A. A. Margolis, P. A. Marmalyuk, A. S. Panfilova, G. A. Yuryev, and P. N. Dumin. 2016. A probabilistic model of adaptive training. Applied Mathematical Sciences 10(48):2369-2380. doi: 10.12988/ams.2016.65168.
- Chumakova, E.V., D.G. Korneev, and M.S. Gasparian. 2022. Razrabotka metoda adaptivnogo testirovaniya na osnove neyrotekhnologiy [Development of adaptive testing method based on neurotechnologies]. Otkrytoe obrazovanie [Open Education] 26(2):4-
13. doi: 10.21686/1818-4243-2022-2-4-13.
- Bosov, A.V., Ya. G. Martyushova, A.V. Naumov, and A. P. Sapunova. 2020. Bayesovskiy podkhod k postroeniyu individual'noy traektorii pol'zovatelya v sisteme distantsionnogo obucheniya [Bayesian approach to the construction of an individual user trajectory in the system of distance learning]. Informatika i ee Primeneniya - Inform. Appl. 14(3):86-93. doi: 10.14357/19922264200313.
- Bosov, A. V. 2022. Primenenie samoorganizuyushchikhsya neyronnykh setey k protsessu formirovaniya individual'noy traektorii obucheniya [Application of self-organizing neural networks to the process of forming an individual learning path]. Informatika i ee Primeneniya - Inform. Appl. 16(3):7-15. doi: 10.14357/19922264220302.
- Bosov, A. V. 2022. Adaptation of Kohonen's self-organizing map to the task of constructing an individual user trajectory in an e-learning system. Data science and algorithms in systems. Eds. R. Silhavy, P. Silhavy, and Z. Prokopova. Lecture notes in networks and systems ser. Cham: Springer. 597:554-564. doi: 10.1007/978-3-031- 21438-7_44.
- Rasch, G. 1980. Probabilistic models for some intelligence and attainment tests. Chicago, IL: The University of Chicago Press. 199 p.
- Kohonen, T. 2001. Self-organizing maps. 3rd ed. Berlin, Heidelberg: Springer. 501 p.
- Repkina, N. G. 2016. Prognozirovanie uspeshnosti obrazovaniya studentov tekhnicheskikh napravleniy podgotovki s ispol'zovaniem iskusstvennykh neyronnykh setey [Prediction of success of educating students of technical arias using artificial neural networks]. Al'manakh mirovoy nauki [Almanac of World Science] 5-1(8):92-95.
- Kazachonak, V. V. 2020. Primenenie neyronnykh setey v obuchenii [Application of neural networks in training]. Informatika i obrazovanie [Informatics and Education] 2(311):41-47. doi: 10.32517/0234-0453-2020-35-2-41-47.
- Okewu, E., P. Adewole, S. Misra, R. Maskeliunas, and R. Damasevicius. 2021. Artificial neural networks for educational data mining in higher education: A systematic literature review. Appl. Artif. Intell. 35(13):983-1021. doi: 10.1080/ 08839514.2021.1922847.
- Shamsutdinova, T. M. 2022. Problemy i perspektivy primeneniya neyronnykh setey v sfere obrazovaniya [Problems and prospects for the application of neural networks for the sphere of education]. Otkrytoe obrazovanie [Open Education] 26(6):4-10. doi: 10.21686/1818-4243-2022-6-4-10.
- Luo, Q., and J. Yang. June 10, 2022. The artificial intelligence and neural network in teaching. Comput. Intel. Neurosc. 1778562. 11 p. doi: 10.1155/2022/1778562.
- Naumov, A. V., A. S. Dzhumurat, and A. O. Inozemtsev. 2014. Sistema distantsionnogo obucheniya matematicheskim distsiplinam CLASS.NET [Distance learning system for mathematical disciplines CLASS.NET]. Vestnik komp'yuternykh i informatsion- nykh tekhnologiy [Herald of Computer and Information Technologies] 10:36-44. doi: 10.14489/vkit.2014.010.pp.036-044.
- SDO MAI CLASS.NET [MAI distance learning system CLASS.NET]. Available at: http://www.distance.mai.ru/ (accessed January 10, 2023)
- Naumov, A. V., and A. O. Inozemtsev. 2013. Algoritm formirovaniya individual'nykh zadaniy v sistemakh distantsionnogo obucheniya [The algorithm for generating the individual tasks in distance learning]. Vestnik komp'yuternykh i informatsionnykh tekhnologiy [Herald of Computer and Information Technologies] 6:46-51.
- Martyushova, Ya. G., and N. M. Lykova. 2018. Organizatsiya refleksivno-otsenochnoy deyatel'nosti studentov universitetov sredstvami elektronnogo uchebnika [Organization of reflexive-evaluative activity of university students by using the learning management system]. Psikhologo-pedagogicheskie issledovaniya [Psychological-Educational Studies] 10(2): 125-134. doi: 10.17759/psyedu.2018100211.
[+] About this article
Title
METHODS OF CLASSIFYING THE DISTANCE LEARNING SYSTEM USERS IN THE MODEL OF CONSTRUCTING THEIR PERS ONALIZED LEARNING STRATEGIES
Journal
Systems and Means of Informatics
Volume 33, Issue 1, pp 68-77
Cover Date
2023-05-11
DOI
10.14357/08696527230107
Print ISSN
0869-6527
Publisher
Institute of Informatics Problems, Russian Academy of Sciences
Additional Links
Key words
finite classification task; cause-and-effect relationships; machine learning in distortions conditions
Authors
Ya. G. Martyushova  , T. A. Mineyeva , and A. V. Naumov
Author Affiliations
Moscow State Aviation Institute (National Research University), 4 Volokolamskoe Shosse, Moscow 125933, Russian Federation
|