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Artificial Intelligence has many applications in which automating a human behavior by machines is one of very important research activities currently in progress. This paper proposes an automated assessment system which uses two novel similarity measures which evaluate students’ short and long answers and compares it with cosine similarity measure and n-gram similarity measure. The proposed system evaluates the information recall and comprehension type answers in Bloom’s taxonomy. The comparison shows that the proposed system which uses two novel similarity measures outperforms the n-gram similarity measure and cosine similarity measure for information recall questions and comprehension questions. The system generated scores are also compared with human scores and the system scores correlates with human scores using Pearson and Spearman’s correlation.

An Automated Assessment System for Evaluation of Students\' Answers Using Novel Similarity Measures