Short answer scoring system using automatic reference answer generation and geometric average normalized-longest common subsequence (GAN-LCS)

The Automatic Short Answer Scoring (ASAS) system is one of the tools that can be used to conduct assessment process on e-learning system. One of the methods applied in the ASAS system is a method for measuring similarities between the reference and student answers. There are two issues to be considered in the assessment process using this method. First, this method should be able to provide a variety of reference answers that can handle the diversity of student answers. Secondly, this method should be able to provide an accurate sentence similarity between the reference answers and student answers. Therefore, two methods are proposed to solve both problems. The first method is to generate a variety of reference answers automatically using Maximum Marginal Relevance (MMR) method, which obtains an accuracy of 91.95%. The second method is to measure accurately sentence similarity between student answers and reference answers that have significantly different length using GAN-LCS. The performance of the proposed method shows an improvement of the Root Mean Square Error (RMSE) value of 0.884 and a correlation value of 0.468.