Voice based answer evaluation system for physically disabled students using natural language processing and machine learning
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 exa...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Saint Petersburg National Research University of Information Technologies, Mechanics and Optics (ITMO University)
2023-04-01
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| Series: | Naučno-tehničeskij Vestnik Informacionnyh Tehnologij, Mehaniki i Optiki |
| Subjects: | |
| Online Access: | https://ntv.ifmo.ru/file/article/21906.pdf |
| _version_ | 1797845399505993728 |
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| author | Meenakshi Thalor Pradeep Mane |
| author_facet | Meenakshi Thalor Pradeep Mane |
| author_sort | Meenakshi Thalor |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-09T17:38:25Z |
| format | Article |
| id | doaj.art-5209e6e93a5c4971936684673e9b187d |
| institution | Directory Open Access Journal |
| issn | 2226-1494 2500-0373 |
| language | English |
| last_indexed | 2024-04-09T17:38:25Z |
| publishDate | 2023-04-01 |
| publisher | Saint Petersburg National Research University of Information Technologies, Mechanics and Optics (ITMO University) |
| record_format | Article |
| series | Naučno-tehničeskij Vestnik Informacionnyh Tehnologij, Mehaniki i Optiki |
| spelling | doaj.art-5209e6e93a5c4971936684673e9b187d2023-04-17T09:26:28ZengSaint Petersburg National Research University of Information Technologies, Mechanics and Optics (ITMO University)Naučno-tehničeskij Vestnik Informacionnyh Tehnologij, Mehaniki i Optiki2226-14942500-03732023-04-0123229930310.17586/2226-1494-2023-23-2-299-303Voice based answer evaluation system for physically disabled students using natural language processing and machine learningMeenakshi Thalor0https://orcid.org/0000-0001-7048-7289Pradeep Mane1https://orcid.org/0000-0002-3252-9983PhD, Associate Professor, AISSMS Institute of Information Technology, Pune, 411001, India, sc 57190340673 PhD, Principal, AISSMS Institute of Information Technology, Pune, 411001, India, sc 36450914400In 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.https://ntv.ifmo.ru/file/article/21906.pdfcosine similaritymachine learningnaive bayesnatural language processingspeech to text conversion |
| spellingShingle | Meenakshi Thalor Pradeep Mane Voice based answer evaluation system for physically disabled students using natural language processing and machine learning Naučno-tehničeskij Vestnik Informacionnyh Tehnologij, Mehaniki i Optiki cosine similarity machine learning naive bayes natural language processing speech to text conversion |
| title | Voice based answer evaluation system for physically disabled students using natural language processing and machine learning |
| title_full | Voice based answer evaluation system for physically disabled students using natural language processing and machine learning |
| title_fullStr | Voice based answer evaluation system for physically disabled students using natural language processing and machine learning |
| title_full_unstemmed | Voice based answer evaluation system for physically disabled students using natural language processing and machine learning |
| title_short | Voice based answer evaluation system for physically disabled students using natural language processing and machine learning |
| title_sort | voice based answer evaluation system for physically disabled students using natural language processing and machine learning |
| topic | cosine similarity machine learning naive bayes natural language processing speech to text conversion |
| url | https://ntv.ifmo.ru/file/article/21906.pdf |
| work_keys_str_mv | AT meenakshithalor voicebasedanswerevaluationsystemforphysicallydisabledstudentsusingnaturallanguageprocessingandmachinelearning AT pradeepmane voicebasedanswerevaluationsystemforphysicallydisabledstudentsusingnaturallanguageprocessingandmachinelearning |