International Conference on E-Learning, E-Education, and Online Training

E-Learning, E-Education, and Online Training pp 35-43 | Cite as

Automation of Variant Preparation and Solving Estimation of Algorithmic Tasks for Virtual Laboratories Based on Automata Model

  • Mikhail S. Chezhin
  • Eugene A. Efimchik
  • Andrey V. Lyamin
Conference paper
Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST, volume 160)

Abstract

In the work a description of an automata model of standard algorithm for constructing a correct solution of algorithmic tests is given. The described model allows a formal determination of the variant complexity of algorithmic test and serves as a basis for determining the complexity functions, including the collision concept – the situation of uncertainty, when a choice must be made upon fulfilling the task between the alternatives with various priorities. The influence of collisions on the automata model and its inner structure is described. The model and complexity functions are applied for virtual laboratories upon designing the algorithms of the variant constructing with a predetermined complexity in real time and algorithms of the procedures of students’ solution estimation with respect to collisions. The results of the work are applied to the development of virtual laboratories, which are used in the practical part of massive online course on graph theory.

Keywords

E-learning Virtual laboratories Remote laboratory control protocol 

References

  1. 1.
    Lyamin, A.V., Vashenkov, O.E.: Virtual environment and instruments for student olympiad on cybernetics. In: Proceedings of 8th IFAC Symposium on Advances in Control Education, pp. 95–100, Kumamoto, Japan (2009)Google Scholar
  2. 2.
    Tao, J., Jing-ying, Z., Lang, W.: The thermal simulation of electromechanical platform system. In: Transportation Electrification Asia-Pacific (ITEC Asia-Pacific), 2014 IEEE Conference and Expo, pp. 1–4, Beijing (2014)Google Scholar
  3. 3.
    Jaffry, D.: Best Practices for Implementing Modeling Guidelines in Simulink, http://www.mathworks.com/company/newsletters/articles/best-practices-for-implementing-modeling-guidelines-in-simulink.html. Mathworks (2014)
  4. 4.
    Efimchik, E.A., Lyamin, A.V.: RLCP-compatible virtual laboratories. In: The International Conference on E-Learning and E-Technologies in Education (ICEEE 2012) Proceedings, pp. 59–64, Lodz, Poland (2012)Google Scholar
  5. 5.
    Xu, L., Huang, D., Tsai, W.-T.: Cloud-based virtual laboratory for network security education. IEEE Trans. Edu. 57(3), 145–150 (2014). IEEE Press, New YorkCrossRefGoogle Scholar
  6. 6.
    Fu, Q., He, K., Ma, X.: Research on experimental skills assessment based on computer simulation technology. J. China Distance Educ. (3), 68–71 (2005). BeijingGoogle Scholar
  7. 7.
    Rodríguez-del-Pino, J.C., Rubio-Royo, E., Hernández-Figueroa, Z.J.: A virtual programming lab for moodle with automatic assessment and anti-plagiarism features. In: Proceedings of the 2012 International Conference on e-Learning, e-Business, Enterprise Information Systems, & e-Government, Las Vegas (2012)Google Scholar
  8. 8.
    Sterbini, A., Temperini, M.: Peer-assessment and grading of open answers in a web-based e-learning setting. In: Information Technology Based Higher Education and Training (ITHET), pp. 1–7, Antalya, Turkey (2013)Google Scholar

Copyright information

© Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2016

Authors and Affiliations

  • Mikhail S. Chezhin
    • 1
  • Eugene A. Efimchik
    • 1
  • Andrey V. Lyamin
    • 1
  1. 1.ITMO UniversitySaint PetersburgRussia

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