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Virtual Dialogue Assistant for Remote Exams

Author

Listed:
  • Anton Matveev

    (Information Technologies and Programming Faculty, ITMO University, 197101 Saint Petersburg, Russia
    These authors contributed equally to this work.)

  • Olesia Makhnytkina

    (Information Technologies and Programming Faculty, ITMO University, 197101 Saint Petersburg, Russia
    These authors contributed equally to this work.)

  • Yuri Matveev

    (Information Technologies and Programming Faculty, ITMO University, 197101 Saint Petersburg, Russia)

  • Aleksei Svischev

    (Information Technologies and Programming Faculty, ITMO University, 197101 Saint Petersburg, Russia)

  • Polina Korobova

    (Information Technologies and Programming Faculty, ITMO University, 197101 Saint Petersburg, Russia)

  • Alexandr Rybin

    (Information Technologies and Programming Faculty, ITMO University, 197101 Saint Petersburg, Russia)

  • Artem Akulov

    (Information Technologies and Programming Faculty, ITMO University, 197101 Saint Petersburg, Russia)

Abstract

A Virtual Dialogue Assistant (VDA) is an automated system intended to provide support for conducting tests and examinations in the context of distant education platforms. Online Distance Learning (ODL) has proven to be a critical part of education systems across the world, particularly during the COVID-19 pandemic. While the core components of ODL are sufficiently researched and developed to become mainstream, there is still a demand for various aspects of traditional classroom learning to be implemented or improved to match the expectations for modern ODL systems. In this work, we take a look at the evaluation of students’ performance. Various forms of testing are often present in ODL systems; however, modern Natural Language Processing (NLP) techniques provide new opportunities to improve this aspect of ODL. In this paper, we present an overview of VDA intended for integration with online education platforms to enhance the process of evaluation of students’ performance. We propose an architecture of such a system, review challenges and solutions for building it, and present examples of solutions for several NLP problems and ways to integrate them into the system. The principal challenge for ODL is accessibility; therefore, proposing an enhancement for ODL systems, we formulate the problem from the point of view of a user interacting with it. In conclusion, we affirm that relying on the advancements in NLP and Machine Learning, the approach we suggest can provide an enhanced experience of evaluation of students’ performance for modern ODL platforms.

Suggested Citation

  • Anton Matveev & Olesia Makhnytkina & Yuri Matveev & Aleksei Svischev & Polina Korobova & Alexandr Rybin & Artem Akulov, 2021. "Virtual Dialogue Assistant for Remote Exams," Mathematics, MDPI, vol. 9(18), pages 1-16, September.
    • Handle: RePEc:gam:jmathe:v:9:y:2021:i:18:p:2229-:d:633310
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    References listed on IDEAS

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    1. Alexandra Witze, 2020. "Universities will never be the same after the coronavirus crisis," Nature, Nature, vol. 582(7811), pages 162-164, June.
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    Cited by:

    1. Yuri Matveev & Olesia Makhnytkina & Pavel Posokhov & Anton Matveev & Stepan Skrylnikov, 2022. "Personalizing Hybrid-Based Dialogue Agents," Mathematics, MDPI, vol. 10(24), pages 1-17, December.

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