Review Of Trending Systems for Automatic Assessment And Scoring Of Student Answers
DOI:
https://doi.org/10.69974/7q7rpa88Keywords:
Optical Character Recognition (OCR), Convolution Neural Network (CNN), K-Nearest Neighbour (K-NN), Recurrent Neural Network (RNN), Support Vector Machine (SVM), Latent Semantic Analysis (LSA)Abstract
There is a need for automation in answer evaluation systems in our modern age, as the globe evolves toward automation. Because online answer evaluation is now only available for mcq-based questions, the checker's job is made more difficult when evaluating theory answers. The teacher carefully checks the answer and assigns the appropriate grade. The existing system necessitates additional staff and time to assess the response. An application based on the evaluation of answers using machine learning is presented in this publication. The paper's goal is to reduce manpower and time usage. Because manual answer evaluation requires significantly more people and time. Also, with the manual approach, it's possible that two identical responses will receive different marks. This paper provides a review for different automated systems.
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