Automatic essay grading system with latent semantic analysis and learning vector quantization

Abstract

Automatic essay grading system called SIMPLE-O (Sistem Penilai Esai Otomatis) that developed by Department of Electrical Engineering, Faculty of Engineering, Universitas Indonesia was built using PHP. This system was developed for helping lecturer assessing the examination, especially with essay form. Currently, the SIMPLE-O is still developed using C programming language for implementing more methods in development that can only be done in that language to improve the performance of the system. To increase the accuracy, Learning Vector Quantization (LVQ) algorithm is implemented in the development due to its ability for supervised classification. The number of data samples in LVQ training phase are affecting the essay scoring performance, less data used will lead to decrease in the result accuracy of the validation phase. Moreover, singular value that generated by Frobenius norm and vector angle pre-processing will also affect the scoring accuracy. But, the number of words-per-column when creating the LSA matrix did not have any significant effect. At the end, SIMPLE-O with LVQ has an average accuracy of 53.57%, 41.66% higher than the system that did not use LVQ. This accuracy performance was still low due to the missing of the words similarity feature. In the previous version of SIMPLE-O, this feature can improve the performance of the essay grading system significantly.