Pedagogical design of a digital teaching assistant in massive professional training for the digital economy
https://doi.org/10.32362/2500-316X-2022-10-3-7-23
Abstract
Objectives. The active digitalization of the Russian economy has resulted in a shortage of IT personnel; this is particularly true of software developers. Thus, the Russian university education is faced with the task of undertaking the large-scale professional training of such specialists. The purpose of the present work was to support the largescale (“massive”) professional training of programmers through the creation and implementation of Digital Teaching Assistant (DTA) computer system, allowing teachers working under stressful conditions to concentrate on functions that require a creative approach, namely, drawing up and discussing nontrivial programming tasks.
Methods. Pedagogical methods for the personification of learning processes were employed. The general approach was based on satisfying the constraints for creating programming task generators. Tasks were generated using methods for generating random programs and data based on probabilistic context-sensitive grammars, along with translation methods using an abstract syntax tree. The declarative representation of the task generator was performed using functional programming methods, allowing the creation of a domain-specific language using combinators. The solutions were checked using automated testing methods.
Results. The developed structure of the proposed DTA system was presented. Considering the automatic generation of programming tasks, classes of practical tasks that reflected the modern specifics of software development were identified along with examples of their generation. A diagram of the programming task generator was provided along with a description of the procedure for automatically checking the solutions of the tasks using a set of program tests generated by the task generator. The presented procedure for comprehensive assessment of a student’s solution included verification of the correctness of the result and plagiarism checks in the case of tasks created manually by the teacher; this also involved validation for compliance with coding style standards, along with metrics for assessing program complexity, etc. The means for recording of statistics of academic achievement of students was characterized along with the interface of interaction between students and teachers.
Conclusions. The experience of introducing a DTA into the learning process of teaching programming in Python confirmed the possibility of personifying the learning process in the form of individual learning paths.
About the Authors
E. G. AndrianovaRussian Federation
Elena G. Andrianova - Cand. Sci. (Eng.), Associated Professor, Head of the Department of Corporate Information Systems, Institute of Information Technologies.
78, Vernadskogo pr., Moscow, 119454, Scopus Author ID 57200555430, ResearcherID T-7908-2018, SPIN-code RSCI 9858-3229
Competing Interests:
not
L. A. Demidova
Russian Federation
Liliya A. Demidova - Dr. Sci. (Eng.), Professor, Professor of the Department of Corporate Information Systems, Institute of Information Technologies.
78, Vernadskogo pr., Moscow,119454, Scopus Author ID 56406258800, ResearcherID R-6077-2016, SPIN-code RSCI 9447-3568
Competing Interests:
not
P. N. Sovetov
Russian Federation
Petr N. Sovetov - Cand. Sci. (Eng.), Associated Professor, Department of Corporate Information Systems, Institute of Information Technologies.
78, Vernadskogo pr., Moscow, 119454, Scopus Author ID 57221375427, SPIN-code RSCI 9999-1460
Competing Interests:
not
References
1. Gudov M.M., Ermakova E.R. Structural transformations of the Russian economy in the conditions of acceleration digitalization of industrial relations. Teoreticheskaya i prikladnaya ekonomika = Theoretical and Applied Economics. 2020;2:1–8 (in Russ.). https://doi.org/10.25136/2409-8647.2020.2.32625
2. Novikova Е.S. Risks and perspectives of higher school transformation for the Russian economy in conditions of gobalization and digitalization. Mezhdunarodnaya torgovlya i torgovaya politika = International Trade and Trade Policy. 2021;7(4):147–162 (in Russ.). https://doi.org/10.21686/2410-7395-2021-3-147-162
3. Yaroslavtseva E.I. Humanitarian aspects of digital technologies. Vestnik Rossiiskogo filosofskogo obshchestva = Russian Philosophical Society. 2020;1–2(91–92): 248–251 (in Russ.). Available from URL: https://rfo1971.ru/wp-content/uploads/2020/03/09-03_248-251.pdf
4. Yaroslavtseva E.I. The potential of digital technologies and the problems of human creativity. Voprosy Filosofii. 2020;11:58–66 (in Russ.). https://doi.org/10.21146/00428744-2020-11-58-66.
5. Strokov A.A. Digitalization of education: problems and prospects. Vestnik Mininskogo universiteta = Vestnik of Minin University. 2020;8(2):15 (in Russ.). https://doi.org/10.26795/2307-1281-2020-8-2-15
6. Khutorskoi A.V. Pedagogical prerequisites for student self-realization in heuristic learning. Vestnik Instituta Obrazovaniya Cheloveka. 2020;1:1 (in Russ.). Available from URL: http://eidos-institute.ru/journal/2020/100/Eidos-Vestnik2020-101-Khutorskoy.pdf
7. Khutorskoi A.V. Interiorization and exteriorization – two approaches to human education. Narodnoe obrazovanie. 2021;1(1484):37–49 (in Russ.).
8. Khalyapina L., Kuznetsova O. Multimedia professional content foreign language competency formation in a digital educational system exemplified by STEPIK framework. Lecture Notes in Networks and Systems. 2020;131: 357–366. https://doi.org/10.1007/978-3-030-47415-7_38
9. Panova I.V., Kolivnyk A.A. An overview of the content of online courses on teaching the basics of programming in the Python language. In: Sovremennye obrazovatel’nye Web-tekhnologii v realizatsii lichnostnogo potentsiala obuchayushchikhsya (Contemporary Educational Web-technologies in the Realization of the Personal Potential of Students): Collection of research articles of international scientific and practical conference. Arzamas; 2020. P. 523–528 (in Russ).
10. Vorob’eva N.A., Oboeva S.V., Bernadiner M.I. Using pedagogical design technologies in the context of digitalization of education. Vestnik Moskovskogo gorodskogo pedagogicheskogo universiteta. Seriya: Informatika i informatizatsiya obrazovaniya = The academic Journal of Moscow City University, series Informatics and Informatization of Education. 2020;1(51):34–37 (in Russ).
11. Guo P.J., Kim J., Rubin R. How video production affects student engagement: An empirical study of MOOC videos. In: Proceedings of the First ACM Conference on Learning @ Scale Conference. 2014. P. 41–50. https://doi.org/10.1145/2556325.2566239
12. Lau S., Guo P.J. Data Theater: A live programming environment for prototyping data-driven explorable explanations. Workshop on Live Programming (LIVE). 2020. 6 p. Available from URL: https://www.samlau.me/pubs/Data-Theater-prototyping-explorable-explanations_LIVE-2020.pdf
13. Guo P.J. Online python tutor: embeddable web-based program visualization for cs education. In: Proceeding of the 44th ACM Technical Symposium on Computer Science Education. 2013. P. 579–584. https://doi.org/10.1145/2445196.2445368
14. Miller H., Willcox K., Huang L. Crosslinks: Improving course connectivity using online open educational resources. The Bridge. 2016;43(3):38–45. Available from URL: http://hdl.handle.net/1721.1/117022
15. Utterberg M.M., et al. Intelligent tutoring systems: Why teachers abandoned a technology aimed at automating teaching processes. In: Proceedings of the 54th Hawaii International Conference on System Sciences. 2021. P. 1538. Available from URL: http://hdl.handle.net/10125/70798
16. Sherman M., et al. Impact of auto-grading on an introductory computing course. J. Comput. Sci. Coll. 2013;28(6):69–75.
17. Rivers K., Koedinger K.R. Data-driven hint generation in vast solution spaces: a self-improving python programming tutor. Int. J. Artif. Intell. Educ. 2017;27(1):37–64. https://doi.org/10.1007/s40593-015-0070-z
18. Sovietov P. Automatic generation of programming exercises. In: 2021 1st International Conference on Technology Enhanced Learning in Higher Education (TELE). IEEE. 2021. P. 111–114. https://doi.org/10.1109/TELE52840.2021.9482762
19. Schleimer S., Wilkerson D.S., Aiken A. Winnowing: local algorithms for document fingerprinting. In: Proceedings of the 2003 ACM SIGMOD International Conference on Management of Data (SIGMOD’03). 2003. P. 76–85. https://doi.org/10.1145/872757.872770
20. Rogers M., et al. Exploring personalization of gamification in an Introductory programming course. In: Proceedings of the 52nd ACM Technical Symposium on Computer Science Education (SIGCSE’21). 2021. P. 1121–1127. https://doi.org/10.1145/3408877.3432402
21. Putnam V., Conati C. Exploring the need for explainable artificial intelligence (XAI) in intelligent tutoring systems (ITS). IUI Workshops. 2019. V. 19. Available from URL: https://explainablesystems.comp.nus.edu.sg/2019/wpcontent/uploads/2019/02/IUI19WS-ExSS2019-19.pdf
22. Shcherbina O.A. Constraint satisfaction and constraint programming. Intellektual’nye sistemy = Intelligent Systems. 2011;15(1–4):53–170 (in Russ.).
23. Wang J., Chen B., Wei L., Liu Y. Skyfire: Data-driven seed generation for fuzzing. In: 2017 IEEE Symposium on Security and Privacy (SP). IEEE. 2017. P. 579–594. https://doi.org/10.1109/SP.2017.23
24. Papadakis M., et al. Mutation testing advances: an analysis and survey. Adv. Comput. 2019;112:275–378. https://doi.org/10.1016/bs.adcom.2018.03.015
25. Hutton G., Meijer E. Monadic Parser Combinators. Technical Report NOTTCS-TR-96-4. Department of Computer Science, University of Nottingham. 1996. 38 p. Available from URL: https://www.cs.nott.ac.uk/~pszgmh/monparsing.pdf
26. Phothilimthana P.M., Sridhara S. High-coverage hint generation for massive courses: Do automated hints help CS1 students? In: Proceedings of the 2017 ACM Conference on Innovation and Technology in Computer Science Education (ITiCSE’17). 2017. Р. 182–187. https://doi.org/10.1145/3059009.3059058
Supplementary files
|
1. Structure of the inline part of the Python programming training course | |
Subject | ||
Type | Исследовательские инструменты | |
View
(68KB)
|
Indexing metadata ▾ |
The Digital Teaching Assistant is an effective help to the teacher in the massive teaching students programming.
Review
For citations:
Andrianova E.G., Demidova L.A., Sovetov P.N. Pedagogical design of a digital teaching assistant in massive professional training for the digital economy. Russian Technological Journal. 2022;10(3):7-23. https://doi.org/10.32362/2500-316X-2022-10-3-7-23