el 16(10): e2

Research Article

Automation of Algorithmic Tasks for Virtual Laboratories Based on Automata Theory

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  • @ARTICLE{10.4108/eai.11-4-2016.151149,
    author={Evgeniy A. Efimchik and Mikhail S. Chezhin and Andrey V. Lyamin},
    title={Automation of Algorithmic Tasks for Virtual Laboratories Based on Automata Theory},
    journal={EAI Endorsed Transactions on e-Learning},
    volume={3},
    number={10},
    publisher={EAI},
    journal_a={EL},
    year={2016},
    month={4},
    keywords={Assessment, Virtual Learning Environments, Massive Open Online Course.},
    doi={10.4108/eai.11-4-2016.151149}
}
  • Evgeniy A. Efimchik
    Mikhail S. Chezhin
    Andrey V. Lyamin
    Year: 2016
    Automation of Algorithmic Tasks for Virtual Laboratories Based on Automata Theory
    EL
    EAI
    DOI: 10.4108/eai.11-4-2016.151149
Evgeniy A. Efimchik1,*, Mikhail S. Chezhin1, Andrey V. Lyamin1
  • 1: ITMO University, Saint Petersburg, Russia
*Contact email: efimchick@cde.ifmo.ru

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 constructing variant with a predetermined complexity in real time and algorithms of the estimation procedures of students’ solution 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
Assessment, Virtual Learning Environments, Massive Open Online Course.
Received
2015-11-10
Accepted
2016-01-13
Published
2016-04-11
Publisher
EAI
http://dx.doi.org/10.4108/eai.11-4-2016.151149

Copyright © 2016 E. A. Efimchik et al., licensed to EAI. This is an open access article distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/3.0/), which permits unlimited use, distribution and reproduction in any medium so long as the original work is properly cited.