Informatics and Applications
2023, Volume 17, Issue 4, pp 32-41
MULTIFACTOR CLASSIFICATION TECHNOLOGY OF MATHEMATICAL CONTENT OF E-LEARNING SYSTEM
Abstract
The article continues the study of the problem of classifying the content of an e-learning system. The previously developed technology for thematic classification of mathematical content contained in the blocks of tasks and examples of e-learning system has been improved and supplemented with new functions. For this purpose, the previously used content model with two properties - a text description of a task and its formula part in TEXformat - has been supplemented with a number of formal numerical attributes, such as the presence of transcendental and derived functions, and the number of formulas in the task. This block of attributes made it possible to improve the quality of the existing thematic classifier and to implement two new ones. The first classifier determines the level of complexity of the task. The second multilabel classifier determines the set of student competencies that the task should form. Such a multifactorial classification is an important stage in the promising direction of the development of e-learning system - automated assessment of the quality of educational content. Performance testing of the proposed algorithms, training of classifiers, and analysis of classification quality were carried out using the tasks from the same discipline of the theory of functions of a complex variable but on significantly expanded set of data, including tasks for independent work of students - calculation and examination tasks.
[+] References (23)
- Martyushova, Ya. G. 2018. Teoreticheskie osnovaniya konstruirovaniya elektronnykh uchebnikov dlya studentov tekhnicheskikh universitetov [Theoretical foundations of designing electronic textbooks for students of technical universities]. Otechestvennaya i zarubezhnaya pedagogika [Domestic and Foreign Pedagogy] 1(5):151-165. doi: 10.24411/2224-0772-2018-10010. EDN: YOCAOT.
- Bosov, A. V., Ya. G. Martyushova, and A. V. Naumov. 2022. Vybor napravleniy otsenivaniya kachestva elektronnykh sredstv obucheniya dlya organizatsii uchebnogo protsessa vuza [Directions selection for assessing the quality of electronic learning tools for the organization of the educational process of the higher educational process]. Sibirskiy pedagogicheskiy zh. [Siberian Pedagogical J.] 2:54-63. doi: 10.15293/1813-4718.2202.05. EDN: KNPIQX.
- Bosov, A. V., and A.V. Ivanov. 2022. Tekhnologiya klassifikatsii tipov kontenta elektronnogo uchebnika [Technology for classification of content types of e-textbooks]. Informatika iee Primeneniya - Inform. Appl. 16(4):63-72. doi: 10.14357/19922264220410. EDN: YERCNH.
- Bosov, A. V., and A.V. Ivanov. 2023. Two approaches to E-Book content classification. Artificial intelligence application in networks and systems. Eds. R. Silhavy and P. Silhavy. Lecture notes in networks and systems ser. Cham, Switzerland: Springer. 724:77-87. doi: 10.1007/978-3- 031-35314-7_6.
- Khutorskoy, A. V. 2003. Didakticheskaya evristika: Teoriya
i tekhnologiya kreativnogo obucheniya [Didactic heuristics: Theory and technology of creative learning]. Moscow: MSU. 416 p.
- Kasprzhak, A. G., ed. 2004. Modernizatsiya obrazovatel'nogo protsessa v nachal'noy, osnovnoy i starshey shkole: varianty resheniya [Modernization of the educational process in primary, secondary and high schools: Solution op-tions]. Moscow: Prosveshchenie. 415p. EDN: QTGWNX.
- Bityukov, Yu. I., and Ya. G. Martyushova. 2022. Reshenie zadach po teorii funktsiy kompleksnogo peremennogo [Solving problems in the theory of functions of a complex variable]. Moscow: MAI. 87 p.
- Ayvazyan, S. A., V. M. Bukhshtaber, I. S. Enyukov, and L. D. Meshalkin. 1989. Prikladnaya statistika: klassifikatsiya i snizhenie razmernosti [Applied statistics: Classification and dimensionality reduction]. Moscow: Finansy i statistika. 608 p.
- Mitchell, T. 1997. Machine learning. New York, NY McGraw-Hill Education. 432 p.
- Zhuravlev, Yu. I., V. V. Ryazanov, and O. V. Sen'ko. 2006. Raspoznavanie. Matematicheskie metody. Programmnaya sistema. Prakticheskie primeneniya [Recognition. Mathematical methods. Software system. Practical applications]. Moscow: Fazis. 159 p.
- Hastie, T., R. Tibshirani, and J. Friedman. 2009. The elements of statistical learning. 2nd ed. New York, NY Springer. 745 p. doi: 10.1007/978-0-387-84858-7.
- Goebel, P.C., B.J. Palik, L. K. Kirkman, M. B. Drew, L. West, and D. C. Pederson. 2001. Forest ecosystems of a Lower Gulf Coastal Plain landscape: Multifactor clas-sification and analysis. J. Torrey Bot. Soc. 128(1):47-75. doi: 10.2307/3088659.
- Shcherbakova, S. A. 2007. Mezhdunarodnyy turizm: ekonomika i geografiya [International tourism: Economics and geography]. Moscow: Finansy i statistika. 144 p.
- Kuleshkov, I. V., V. S. Kolbikov, and K. S. Basniev. 2010. Klassifikatsiya neftegazovykh ekspluatatsionnykh ob"ektov s otsenkoy stepeni slozhnosti vyrabotki zapasov uglevodorodov [Oil and gas production facilities classifica-tion with complexity of hydrocarbon reserves production assessment]. Tekhnologii nefti i gaza [Oil and Gas Tech-nologies] 2(67):29-38. EDN: LKYHCP.
- Zadorozhneva, Yu. V., and A. E. Kalinina. 2012. Mnogofaktornaya otsenka effektivnosti realizatsii sotsial'no- ekonomicheskoy politiki regiona [Multifactor assessment of efficiency realization of socio-economic policy of the region]. Sovremennyye problemy nauki i obrazovaniya [Modern Problems of Science and Education] 4:192-192. EDN: PBIRUD.
- Kazarin, O.V., R. A. Sharyapov, and V. V. Yashchenko.
2018. Mnogofaktornaya klassifikatsiya ugroz informatsionnoy bezopasnosti kiberfizicheskikh sistem [Multifactorial classification of threats to information security of cyber-physical systems]. Vestnik RGGU. Ser. Informati-ka. Informatsionnaya bezopasnost'. Matematika [RGGU Bulletin. Ser. Information science. Information security. Mathematics] 1:39-55. EDN: FNXARP.
- Xiong, Y., L. Wang, Q. Wang, S. Liu, and B. Kou. 2022. Improved convolutional neural network with feature selection for imbalanced ECG multi-factor classification. Measurement 189:110471. doi: 10.1016/j. measurement.2021.110471.
- Kaftannikov, I.L., and A.V. Parasich. 2016. Problemy formirovaniya obuchayushchey vyborki v zadachakh mashinnogo obucheniya [Problems of training set's for-mation in machine learning tasks]. Vestnik YuUrGU. Ser. Komp'yuternye tekhnologii, upravlenie, radioelektron- ika [Bulletin of the South Ural State University. Ser. Computer technologies, automatic control, radio elec-tronics] 16(3):15-24. doi: 10.14529/ct"sr160302. EDN: TZWHTS.
- Sechidis, K., G. Tsoumakas, and I. Vlahavas. 2011. On the stratification of multi-label data. Machine learning and knowledge discovery in databases. Eds. D. Gunopulos, T. Hofmann, D. Malerba, and M. Vazirgiannis. Lecture notes in computer science ser. Berlin, Heidelberg: Springer. 6913:145-158. doi: 10.1007/978-3-642-23808- 6_10.
- Breiman, L. 2001. Random forests. Mach. Learn. 45:5-32. doi: 10.1023/A:1010933404324.
- Haykin, S. 2009. Neural networks and learning machines. 3rd ed. Cranbury, NJ: Pearson Education. 906 p.
- Bogatinovski, J., L. Todorovski, S. Dzeroski, and D. Kocev. 2022. Comprehensive comparative study of multilabel classification methods. ExpertSyst. Appl. 203:117215. 23 p. doi: 10.1016/j.eswa.2022.117215.
- Hosmer, D. W, S. Lemeshow, and R. X. Sturdivant. 2013. Applied logistic regression. 3rd ed. New York, NY: Wiley. 528 p. doi: 10.1002/9781118548387.
[+] About this article
Title
MULTIFACTOR CLASSIFICATION TECHNOLOGY OF MATHEMATICAL CONTENT OF E-LEARNING SYSTEM
Journal
Informatics and Applications
2023, Volume 17, Issue 4, pp 32-41
Cover Date
2023-12-10
DOI
10.14357/19922264230405
Print ISSN
1992-2264
Publisher
Institute of Informatics Problems, Russian Academy of Sciences
Additional Links
Key words
e-learning system; mathematical content; machine learning; multifactor classification; content quality assessment
Authors
A. V. Bosov  and A. V. Ivanov
Author Affiliations
Federal Research Center "Computer Science and Control" of the Russian Academy of Sciences, 44-2 Vavilov Str., Moscow 119333, Russian Federation
|