Abstract
Many examinations, such as competitive, intuitive, non-institutional examinations that students apply for, are carried out every year. In most cases, competitive and entry examinations contain objective or multiple-choice questions. Such tests are evaluated and carried out on the device, and their evaluation is therefore straightforward. However, since these examinations address multiple-choice questions only, there is still no ability to answer and evaluate descriptive questions. If the method of assessing descriptive responses is automated to effectively evaluate the student’s examination response sheets, it will be very helpful for academic institutions. A new method is suggested in this study to evaluate the short answers of the students, such as descriptive answers using algorithms from natural language processing [NLP]. The staff member develops a response sheet and keyword dataset for the examination process in this system. In data storage, such datasets are stored and students enter their answers on the examination page. Using NLP algorithms, this system calculates results automatically. Before this assessment process, the preprocessing technique was applied to the responses provided by the students.
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Rambola, R.K., Bansal, A., Savaliya, P., Sharma, V., Joshi, S. (2021). Development of Novel Evaluating Practices for Subjective Answers Using Natural Language Processing. In: Singh Pundir, A.K., Yadav, A., Das, S. (eds) Recent Trends in Communication and Intelligent Systems. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-16-0167-5_21
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DOI: https://doi.org/10.1007/978-981-16-0167-5_21
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