Evaluation of subjective or text based answers has been a hurdle in the development of eLearning systems since a very long time. The problem related to evaluation of subjective answers is that each student has his/her own way of answering and it is difficult to determine the degree of correctness.
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It involves evaluation of grammar and knowledge concepts using the conceived interpretation and creativity of a human mind. The person operating the machine should have the subject knowledge on which the paper is to be evaluated.
Our Proposed System uses machine learning and NLP to solve this problem.
Typically, there are two types of question answering systems:
Closed-domain question answering
Open-domain question answering
There exist two methods for querying the answer for user questions
The methodologies used in this project are machine learning and NLP
Procedure
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The only disadvantage of using computer as evaluation tool is the possibility of failure at the time of submission of answers and lack of technical knowledge provided to the teachers and students
There are three performance evaluation based question answering systems
Objectives
Software Requirements:
Hardware Requirements:
Conclusion
Considering all approaches and downfalls in each approach, the different parameters extracted and proposed an approach to evaluate short answers for descriptive subjects automatically
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