Informatics and Applications
2020, Volume 14, Issue 3, pp 86-93
BAYESIAN APPROACH TO THE CONSTRUCTION OF AN INDIVIDUAL USER TRAJECTORY IN THE SYSTEM OF DISTANCE LEARNING
- A. V. Bosov
- Ya. G. Martyushova
- A. V. Naumov
- A. P. Sapunova
Abstract
The paper considers the task of forming an individual user path for a distance learning system (LMS) with a mixed form of conducting educational activities with organization of independent work of students using LMS. At the end of each section of the training course, the LMS users are divided into categories determined by the solution of the Bayesian classification problem. For each category, an individual task of a different level of complexity is proposed for the next section of the course, thus forming the individual trajectory of the student. The Bayesian classifier is set up based on statistics of work of the users of LMS. The experimental results of solving the problem at one of the stages of training are presented.
[+] References (16)
- Rasch, G. 1980. Probabilistic models for some intelligence and attainment tests. Chicago, IL: University of Chicago Press. 224 p.
- Van der Linden, W.J., D.J. Scrams, and D. L. Schnip- ke. 1999. Using response-time constraints to control for differential speededness in computerized adaptive testing. Appl. Psych. Meas. 23(3):195-210.
- 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]. Modelling Data Analysis 1:4-28.
- Kibzun, A. I., and A. O. Inozemtsev. 2014. Using the maximum likelihood method to estimate test complexity levels. Automat. Rem. Contr. 75(4):607-621.
- Kuravsky, L. S., P. A. Marmalyuk, G. A. Yuryev, P. N. Dumin, and A. S. Panfilova. 2015. Veroyatnostnoe modelirovanie protsessa vypolneniya testovykh zadaniy na osnove modifitsirovannoy funktsii Rasha [Probabilistic modeling of the test tasks based on the modified Rasch function]. Voprosy psikhologii [Psychology Issues] 4:109-118.
- 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 Math-ematical Sciences 10(48):2369-2380.
- Naumov, A. V., and G.A. Mkhitaryan. 2016. On the problem of probabilistic optimization of time-limited testing. Automat. Rem. Contr. 77(9):1612-1621.
- Naumov, A.V., G.A. Mkhitaryan, and E. E. Cherygova. 2019. Stokhasticheskaya postanovka zadachi formirovaniya testa zadannogo urovnya slozhnosti s mini- mizatsiey kvantili vremeni vypolneniya [Stochastic statement of the problem of generating tests with defined complexity with the minimization of quantile of test passing time]. Vestnik komp'yuternykh i informatsionnykh tekhnologiy [Herald of Computer and Information Technologies] 2:37-46.
- Callan, R. 1999. The essence of neural networks. Prentice Hall Europe. 232 p.
- Vorob'ev, E.V., and E.V Puchkov. 2017. Klassifikatsiya tekstov s pomoshch'yu svertochnykh neyronnykh setey [Classification of texts using convo-lutional neural networks]. Molodoy issledovatel' Dona [Young Researcher of the Don] 6(9):2-7. Available at: http://mid-journal.ru/upload/iblock/8ed/1- vorobev_-puchkov.pdf (accessed July 23, 2020).
- Mikryukov, A. A., A.V Babash, and V.A. Sizov. 2019. Klassifikatsiya sobytiy v sistemakh obespecheniya informatsionnoy bezopasnosti na osnove neyrosetevykh tekhnologiy [Classification of events in information security systems based on neural networks]. Otkrytoe obra- zovanie [Open Education] 23(1):57-63.
- D'yakonov, A. G. 2015. Solution methods for classification problems with categorical attributes. Computational Mathematics Modeling 26(3):408-428.
- Vorontsov, K. V 2015. Lektsii po algoritmicheskim kompozitsiyam [Lectures on algorithmic compositions]. Available at: http://www.ccas.ru/voron/download/ Composition.pdf (accessed July 23, 2020).
- 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.
- Naumov, A. V., A. S. Dzhumurat, and A. O. Inozemtsev. 2014. Sistema distantsionnogo obucheniya matematiche- skim distsiplinam CLASS.NET [Distance learning system for mathematical disciplines CLASS.NET]. Vestnik komp'yuternykh i informatsionnykh tekhnologiy [Herald of Computer and Information Technologies] 10:36-44.
[+] About this article
Title
BAYESIAN APPROACH TO THE CONSTRUCTION OF AN INDIVIDUAL USER TRAJECTORY IN THE SYSTEM OF DISTANCE LEARNING
Journal
Informatics and Applications
2020, Volume 14, Issue 3, pp 86-93
Cover Date
2020-09-30
DOI
10.14357/19922264200313
Print ISSN
1992-2264
Publisher
Institute of Informatics Problems, Russian Academy of Sciences
Additional Links
Key words
distance learning system; Bayesian classifier; adaptive systems; individual learning path
Authors
A. V. Bosov , , Ya. G. Martyushova , A. V. Naumov , and A. P. Sapunova
Author Affiliations
Institute of Informatics Problems, Federal Research Center "Computer Science and Control" of the Russian Academy of Sciences, 44-2 Vavilov Str., Moscow 119333, Russian Federation
Moscow State Aviation Institute (National Research University), 4 Volokolamskoe Shosse, Moscow 125933, Russian Federation
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