Automated essay scoring using Generalized Latent Semantic Analysis

Abstract

Automated Essay Grading (AEG) is a very important research area in educational technology. Latent Semantic Analysis (LSA) is an information retrieval technique used for automated essay grading. LSA forms a word by document matrix and then the matrix is decomposed using Singular Value Decomposition (SVD) technique. Existing AEG systems based on LSA cannot achieve higher level of performance to be a replica of human grader. We have developed an AEG system using Generalized Latent Semantic Analysis (GLSA) which makes n-gram by document matrix instead of word by document matrix. We have evaluated this system using details representation and showed the performance of the system. Experimental results show that our system outperforms the existing system.