An Analysis of Automated Answer Evaluation Systems based on Machine Learning

Evaluation of the answers remain as one of the most important factors in the learning and teaching process. Automatic evaluation of the answers is very necessary thus, many system has been developed in this digital era. Usually, the subjective answers are in either short form or long answers. The existing system available for evaluation has shown mediocre result in evaluating and scoring the answers. In such frameworks, the data recovery technique to gauge likeness between understudies answer and references answer is utilized, yet such scoring framework doesn't give the best outcome yet. There are very few keywords available in short answers. The answers with such limited number of keywords needs special care, especially while calculating the weighting score of the answers. In the presented study, we try to summarize the existing mechanism and analyses the performance of the system used for automatic grading of the long and descriptive answers.