UDC [378:62]-042.4:004
https://doi.org/10.20339/AM.06-23.094
Ilya B. Ginzburg, Cand. Sc. (Technic), Docent at Moscow Aviation Institute (National Research University)
Aleksandr A. Ermakov*, Cand. Sc. (Economic), Docent at Moscow Aviation Institute (National Research University), e-mail: iliagi@mail.ru , aleral@mail.ru , snp@inmas.ru
Sergey N. Padalko, Dr. Sc. (Technic), Professor at Moscow Aviation Institute (National Research University)
The article presents the results of the development and implementation of distance learning technologies for conducting a laboratory workshop in engineering disciplines. Among them, ways to ensure remote laboratory practice, organizational and technical problems associated with distance learning, and solutions to these problems based on the experience gained. The technology and software solution based on Project Jupiter components are proposed, providing effective remote interaction in the process of conducting laboratory classes.
Keywords: distance learning, laboratory workshop, problems of distance learning, software, Project Jupyter, the effectiveness of remote interaction.
References
1. Moodle — Open-source learning platform. Moodle.org [Online resource] URL: https://moodle.org/
2. SDE “Prometei”. Virtual Technologies in Education LLC [Online resource] URL: https://www.prometeus.ru/
3. Borodinsky, A.V., Ginzburg, I.B., Stolyarchuk, V.A. Evolution of distance learning methods. Scientific and Technical Bulletin of the Volga region. 2020. No. 11. P. 24–26.
4. Maxorin, А.О., Padalko, С.N., Terentiev, М.N. Simulation of a multi-gateway wireless self-organizing network of arbitrary topology taking into account the solution of the problem of choosing optimal network parameters. Scientific and Technical Bulletin of the Volga region. 2014. No. 6. P. 228–232.
5. Maxorin, А.О. Integer modeling of injective and bijective mappings. Scientific and Technical Bulletin of the Volga region. 2019. No. 12. P. 77–79.
6. Maxorin, А.О., Stankevich, А.М. Resource usage model for solving aggregate planning tasks. Scientific and Technical Bulletin of the Volga region.2017. No. 2. P. 116–120.
7. Stolyarchuk, V.А. Automated research support system. Automation. Modern technologies. 2020. No. 2. P. 64–71.
8. Kondrashev, Y.N. The use of analytical technologies in decision support systems at the stages of the life cycle of aerospace products. Scientific and Technical Bulletin of the Volga Region. 2019. No. 5. P. 37–41.
9. Volkomorov, S.V., Karpenko, А.P., Martinyk, V.А. Analysis of kinematics of parallel mechanisms by means of the CATIA computer-aided design system. Information Technologies. 2010. No. 11. P. 45–51.
10. Project Jupyter [Online resource]. URL: https://jupyter.org/
11. JupyterHub – JupyterHub 3.0.0 documentation [Online resource]. URL: https://jupyterhub.readthedocs.io/en/stable/
12. JupyterLab – JupyterLab 3.5.0b0 documentation [Online resource]. URL: https://jupyterlab.readthedocs.io/en/stable/
13. gnuplot homepage [Online resource]. URL: http://www.gnuplot.info/
14. GNU Octave [Online resource]. URL: https://octave.org/
15. QuantStack/jupyterlab-drawio: A standalone embedding of the FOSS drawio / mxgraph package into jupyterlab // GitHub: Let’s build from here [Online resource] URL: https://github.com/QuantStack/jupyterlab-drawio
16. Jeffrey M. Perkel. Why Jupyter is data scientists’ computational notebook of choice. November 2018. Nature 563 (7729). P. 145–146. DOI:10.1038/d41586-018-07196-1
17. Adam B. Blake, Benjamin J. Winjum, James Stigler. Simple Workflows for Teaching with Jupyter. January 2022. Conference: IASE 2021 Satellite Conference: Statistics Education in the Era of Data Science. DOI:10.52041/iase.yphve
18. Noprianto Noprianto, Vivi Nur Wijayaningrum, Vivin Ayu Lestari. Jupyter Lab Platform-Based Interactive Learning. December 2022. Conference: 2022 International Conference on Electrical and Information Technology (IEIT) At: Malang, Indonesia. DOI:10.1109/IEIT56384.2022.9967857
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