7
Milan D. Popov
Kostroma State University
Anna A. Loginova
Kostroma State University
Artem R. Denisov
Kostroma State University
A TOOL FOR REVEALING BEHAVIOUR PATTERNS OF KOSTROMA STATE UNIVERSITY STU-DENTS BASED ON PROCESS MINING ALGORITHMS
Popov M. D., Loginova A. A., Denisov A. R. A tool for revealing behaviour patterns of Kostroma State University students based on PROCESS MINING algorithms. Technologies & Quality. 2022. No 3(57). P. 34–38. (In Russ.) https://doi.org//10.34216/2587-6147-2022-3-57-34-38.
DOI: https://doi.org/10.34216/2587-6147-2022-3-57-34-38
УДК: 004.415.2
EDN: PTYANG
Publish date: 2022-10-07
Annotation: This article deals with the problem of the formation of student competences. It is proposed to trans-form the educational programme into a system of educational results by applying the methods of Educational Process Mining. As part of the study, the architecture of the system for analysing digital traces of students is proposed. Such a system will make it possible to analyse the activity of students in the distance learning system and in the future to identify similar behavioural patterns. Data from the LMS Moodle is subject to analysis, namely the tasks handed in by students and the actions they perform in the system. The implementation of this architecture will allow, based on the log data of the Moodle system, solving the problem of choosing the most appropriate competences for the student in accordance with its identified patterns of behaviour in the information environment.
Keywords: behaviour pattern, tool, Moodle, Process mining, student competences formation, digital footprint, behaviour patterns, decision support system
Literature list: 1. Evaluation of educational results based on students' competencies*. URL: https://science-education.ru/ru/article/view?id=27188 (date of access: 12.09.2022) (In Russ.) 2. Digital footprint. Digital Footprint Standard University 20.35*. URL: https://standard.2035.university/ (date of access: 09.09.2022) (In Russ.) 3. Vinogradova D. A., Krasavina M. S. Prototyping an information system for automatic monitoring of student motivation. Tekhnologii i kachestvo [Technologies and quality]. 2020;3(49):25–29. (In Russ.) 4. Learning Management System. A large overview of LMS systems: types, suppliers and a real implementation case*. URL: https://vc.ru/education/218817-bolshoy-obzor-lms-sistem-vidy-postavshchiki-i-realnyy-keys-vnedreniya (date of access: 12.09.2022) (In Russ.) 5. Process Mining. A resource describing the concept of Process Mining*. URL: https://habr.com/ru/post/ 244879 (date of access: 12.09.2022) (In Russ.) 6. Loginova A. A., Denisov A. R. Application of the technology analysis of the digital footprint to create a system for forming the individual digital profile of the student. BIG DATA i analiz vysokogo urovnya [BIG DATA and Advanced Analytics], 2022. (In Russ.) 7. Hachicha W., Ghorbel L., Champagnat R., Zayani C. A., Amous I. Using Process Mining for Learning Resource Recommendation: A Moodle Case Study. Procedia Computer Science 2021;192:853–862. 8. Van der Aalst W. Process mining: Data science in action. Berlin, Heidelberg, Springer-Verlag. 2016. 477 р. 9. Bogarín A., Cerezo R., Romero C. A survey on educational process mining. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery. 2018;8,1:1230–1247. 10. Bogarín, A., Cerezo, R., Romero, C. Discovering learning processes using Inductive Miner: A case study with Learning Management Systems (LMSs). Psicothema. 2018;30,3:322–329. 11. Moodle. Moodle Development Guide*. URL: https://docs.moodle.org/dev/Process (date of access: 12.09.2022). 12. Van der Aalst W., Guo S., Gorissen P. Comparative Process Mining in Education. Approach Based on Process Cubes. 3rd International Symposium on Data-Driven Process Discovery and Analysis (SIMPDA). Riva del Garda. 2013:110–134. URL: https://hal.inria.fr/hal-01746404/file/335156_1_En_6_Chapter.pdf (Accessed 31.10.2022). 13. Gunther C. W., van der Aalst W. M. P. Fuzzy Mining – Adaptive Process Simplification Based on Multi-perspective Metrics. Proceedings of the 5th International Conference on Business Process Management. Lecture Notes in Computer Science. 2007;4714:328–343. 14. Awatef Hicheur Cairns, Billel Gueni, Mehdi Fhima, Andrew Cairns, Stéphane David. Process Mining in the Education Domain. International Journal on Advances in Intelligent Systems. 2015;8,1-2:219–232. 15. Aggarwal C. An Introduction to social network data analytics. Social Network Data Analytics. 2011. URL: http://charuaggarwal.net/socialintro.pdf (Accessed 31.10.2022). 16. Van der Aalst W. M. P., de Medeiros A. K. A., Weijters A. J. M. M. Genetic Process Mining. Applications and Theory of Petri Nets : 26th International Conference (June 20–25, 2005. Miami). 2005:48–69. 17. Van der Aalst W. M. P., Weijters A. J. M. M., Maruster L. Workflow Mining: Discovering Process Models from Event Logs. IEEE Transactions on Knowledge and Data Engineering. 2004;16(9):1128–1142. 18. Weijters A. J. M. M, van der Aalst W. M. P., de Medeiros A. A. K. Process Mining with the Heuristics Miner-algorithm. BETA Working Paper Series, WP 166. Eindhoven : Eindhoven University of Technology, 2006. URL. https://www.researchgate.net/publication/306014995_Process_mining_with_the_heuristics_ miner-algorithm (Accessed 31.10.2022). 19. JetBrains. PyCharm. Software Development Tools Distribution Resource*. URL: https://www.jetbrains. com/idea (Accessed: 15.09.2022). (In Russ.)
Author's info: Milan D. Popov, Kostroma State University, Kostroma, Russia, milan070699@gmail.com, https://orcid.org/0000-0001-6580-4614
Co-author's info: Anna A. Loginova, Kostroma State University, Kostroma, Russia, aloginova255@gmail.com, https://orcid.org/0000-0001-8306-4373
Co-author's info: Artem R. Denisov, Kostroma State University, Kostroma, Russia, iptema@yandex.ru, https://orcid.org/0000-0002-3359-4103