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Editor-in-Chief
Nikiforov
Vladimir O.
D.Sc., Prof.
Partners
doi: 10.17586/2226-1494-2023-23-2-299-303
Voice based answer evaluation system for physically disabled students using natural language processing and machine learning
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Abstract
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Thalor M., Mane P. Voice based answer evaluation system for physically disabled students using natural language processing and machine learning. Scientific and Technical Journal of Information Technologies, Mechanics and Optics, 2023, vol. 23, no. 2, pp. 299–303. doi: 10.17586/2226-1494-2023-23-2-299-303
Abstract
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.
Keywords: cosine similarity, machine learning, Naive Bayes, natural language processing, speech to text conversion
References
References
-
Abhishek Girkar, Mohit khambayat, Ajay Waghmare, Supriya Chaudhary. Subjective answer evaluation using natural language processing and machine learning. International Research Journal of Engineering and Technology (IRJET), 2021, vol. 8, no. 4, pp. 5040–5043.
-
Shweta M. Patil, Sonal Patil. Evaluating student descriptive answers using natural language processing. International Journal of Engineering Research & Technology (IJERT), 2014, vol. 3, no. 3, pp. 1716–1718.
-
Mercy R.A. Automated explanatory answer evaluation using machine learning approach. Design Engineering (Toronto), July 2021.
-
Lakshmi V., Ramesh V. Evaluating students’ descriptive answers using natural language processing and artificial neural networks. IJCRT, 2017, vol. 5, no. 4, pp. 3168–3173.
-
Mittal H., Devi M.S. Computerized evaluation of subjective answers using hybrid technique. Advances in Intelligent Systems and Computing, 2016, vol. 413,pp. 295–303. https://doi.org/10.1007/978-981-10-0419-3_35
-
Sakhapara A., Pawade D., Chaudhari B., Gada R., Mishra A. Bhanushali S. Subjective answer grader system based on machine learning. Advances in Intelligent Systems and Computing, 2019, vol. 898,pp. 347–355. https://doi.org/10.1007/978-981-13-3393-4_36
-
Mahmud T.A., Hussain M.G., Kabir S., Ahmad H., Sobhan M. A keyword based technique to evaluate broad question answer script. Proc. of the 9th International Conference on Software and Computer (ICSCA),2020, pp. 167–171. https://doi.org/10.1145/3384544.3384604
-
BanoS.,JithendraP., Niharika G.L., SikhiY. Speech to text translation enabling multilingualism. Proc. of the IEEE International Conference for Innovation in Technology (INOCON),2020.https://doi.org/10.1109/inocon50539.2020.9298280
-
Bhatia M.S., Aggarwal A., Kumar N. Speech-to-text conversion using GRU and one hot vector encodings. Palarch’s Journal of Archaeology of Egypt/Egyptology,2020,vol. 17, no. 9,pp. 8513–8524.
-
Johri E.,Dedhia N.,Bohra K.,Chandak P., Adhikari H. ASSESS - automated subjective answer evaluation using semantic learning.Proc. of the 4th International Conference on Advances in Science & Technology (ICAST2021), 2021. http://dx.doi.org/10.2139/ssrn.3861851
-
Deotare S., Khan R.A. Automatic online subjective text evaluation using text mining. International Journal of Recent Technology and Engineering (IJRTE), 2019, vol. 8,no. 2,pp. 1238–1242. https://doi.org/10.35940/ijrte.I8725.078219
-
Bahel V., Thomas A. Text similarity analysis for evaluation of descriptive answers. arXiv, 2021,arXiv:2105.02935.https://doi.org/10.48550/arXiv.2105.02935
-
Meenakshi Anurag T., Pradeep B.M., Vishaka M. Web app for quick evaluation of subjective answers using natural language processing.Scientific and Technical Journal of Information Technologies, Mechanics and Optics, 2022, vol. 22, no. 3, pp. 594–599. https://doi.org/10.17586/2226-1494-2022-22-3-594-599
-
Mandge V.A., Thalor M.A. Revolutionize cosine answer matchingtechnique for question answering system. Proc. of the InternationalConference on Emerging Smart Computing and Informatics (ESCI), 2021, pp. 335–339. https://doi.org/10.1109/ESCI50559.2021.9396864
-
Rish I. An empirical study of the naïve bayes classifier. IJCAI Work Empir Methods Artificial Intelligence. V. 3, 2001.
