Subtractive Neuro-Fuzzy modeling techniques applied to short essay auto- grading problem

Abstract

Automated Essay Grading (AEG) systems that are currently available use different techniques to extract specific written dimensions features to assess the written prose. Several Neuro-Fuzzy approaches have been applied to try to solve the short essay AEG problem. The main idea behind this approach is to identify the number of main keywords (5 inputs) each of which has 4 synonyms based on specific constraints. These inputs have been processed by developing two general models including; Artificial Neural Network Back-propagation optimization technique and Subtractive Clustering technique. Furthermore a third general model have developed to incorporate more main keywords by processing double the number of previous models input, single output with specific constraints by combining Subtractive clustering and Neuro Fuzzy techniques. The Sub-Clustering Neuro Fuzzy model could be used for a variable number of inputs. More than 900 generated datasets have been used representing questions related to information technology field. The correlation coefficient have been used to measure the agreements between the actual and predicted marks of the models, and used two error measures including; Mean Absolute Percentage Error (MAPE) and Root Mean Square Error (RMSE) to indicate the performance of the developed models. The average results obtained from all three general models indicates the suitability and adequacy of the developed models to solve the short essay AEG problem and shows that the preliminary results are promising.