ntvНаучно-технический вестник информационных технологий, механики и оптикиScientific and Technical Journal of Information Technologies, Mechanics and Optics2226-14942500-0373Университет ИТМО10.17586/2226-1494-2023-23-2-299-303ntv-375Research ArticleКОМПЬЮТЕРНЫЕ СИСТЕМЫ И ИНФОРМАЦИОННЫЕ ТЕХНОЛОГИИCOMPUTER SCIENCEГолосовая система оценки ответов для учащихся с ограниченными физическими возможностями, использующих обработку естественного языка и машинное обучениеVoice based answer evaluation system for physically disabled students using natural language processing and machine learninghttps://orcid.org/0000-0001-7048-7289ТалорМ.ThaloM.

Талор Минакши — PhD, доцент

Пуне, 411001

sc 57190340673

Талор Минакши — PhD, доцент

Пуне, 411001

sc 57190340673

meenakshi.thalor@aissmsioit.orghttps://orcid.org/0000-0002-3252-9983МанеП.ManeP.

Мане Прадип — PhD, руководитель

Пуне, 411001

sc 36450914400

Мане Прадип — PhD, руководитель

Пуне, 411001

sc 36450914400

pbmane6829@gmail.comAISSMS Институт информационных технологийИндияAISSMS Institute of Information TechnologyIndia202320122024232299303Copyright © Талор М., Мане П., 20242024Талор М., Мане П.Thalo M., Mane P.Данная работа распространяется под лицензией Creative Commons Attribution 4.0.This work is licensed under a Creative Commons Attribution 4.0 License.https://ntv.elpub.ru/jour/article/view/375

В современном образовательном процессе возникает потребность в автоматизации систем оценки ответов. Задача проверяющего усложняется при анализе теоретических ответов, так как их онлайн-оценка доступна только для вопросов с несколькими вариантами ответов. Преподаватель тщательно изучает ответ, прежде чем поставить соответствующую оценку. Для изучения ответов существующий подход требует дополнительных сотрудников и времени. В работе представлено приложение, основанное на оценке ответов с использованием обработки естественного языка и машинного обучения, которое включает голосовую подсказку для слабовидящих учащихся. Приложение автоматизирует процесс проверки субъективных ответов, рассматривая извлечение текста и признаков, а также классификацию баллов. Мерами оценки являются сходства: Term Frequency Inverse Document Frequency (TF-IDF), векторов, ключевых слов и грамматики, которые рассматриваются для определения общего сходства между результатом учителя и оценкой системы. Полученные результаты показали, что система оценивает ответы с точностью 95 %. Предлагаемая методика предназначена для оценки результатов экзаменов учащихся, не умеющих писать, но умеющих говорить. Применение разработанного приложения позволит сократить затраты труда и времени преподавателя за счет сокращения ручного труда.

In the modern educational process, there is a need to automate response assessment systems. The task of the reviewer becomes more difficult when analyzing theoretical answers, because online assessment of answers is available only for questions with multiple choice answers. The teacher carefully examines the answer before giving the appropriate mark. The existing approach requires additional staff and time to study the responses. This article introduces a natural language processing and machine learning response-based app that includes a voice prompt for visually impaired students. The application automates the process of checking subjective responses by considering text extraction, feature extraction, and score classification. Evaluation measures, such as Term Frequency-Inverse Document Frequency (TF-IDF) similarity, vector similarity, keyword similarity, and grammar similarity, are considered to determine the overall similarity between teacher outcome and system evaluation. The conducted experiments showed that the system evaluates the answers with an accuracy of 95 %. The proposed methodology is designed to assess the results of exams for students who cannot write but who can speak. The application of the developed application allows reducing the labor costs and time of the teacher by reducing manual labor.

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The authors declare that there are no conflicts of interest present.