Abstract:
Automated Essay Grading (AEG) is a very important research area in educational
assessment. Several AEG systems have been developed using statistical, Bayesian Text
Classification Technique, Natural Language Processing (NLP), Artificial Intelligence (AI),
and amongst many others. Latent Semantic Analysis (LSA) is an information retrieval
technique used for automated essay grading. LSA forms a word by document matrix and the
matrix is decomposed using Singular Value Decomposition (SVD) technique. It does not
consider the word order in a sentence. Existing AEG systems based on LSA cannot achieve
higher level of performance to be a replica of human grader. Moreover most of the essay
grading systems are used for grading pure English essays or essays written in pure European
languages.
We have developed a Bangla essay grading system using Generalized Latent Semantic
Analysis (GLSA) which uses n-gram by document matrix instead of word by document
matrix of LSA.
We have also developed an architecture for training essay set generation and evaluation of
submitted essays by using the training essays. We have evaluated this system using real and
synthetic datasets. We have developed training essay sets for three domains: standard Bangla
essays titled “বাংলােদেশর sাধীনতা সংgাম”, “কািরগির িশkা” and descriptive answers of S.S.C level
Bangla literature. We have gained 89% to 95% accuracy compared to human grader. This
accuracy level is higher than that of the existing AEG systems.
