Soft computing auto essay grading
by Labib Arafeh
International Journal of Continuing Engineering Education and Life-Long Learning (IJCEELL), Vol. 23, No. 2, 2013

Abstract: Soft computing techniques try to mimic the humans in reasoning, learning and prediction. We have implemented these techniques to address the auto essay grading, particularly for short-answer types. A knowledge extraction approach is proposed and then assessed using samples of short-answer essays. It is based on identifying keywords and their synonyms and providing the sub-grades that are summed to get the total grade. The soft computing modelling techniques have been used to enhance the KE method. These modelling techniques include the fuzzy logic - Mamdani and Sugeno models, the neural networks models with both the subtractive clustering and back propagation learning rules and the neuro fuzzy model. The same samples have been used to implement the modelling techniques. The overall obtained correlation coefficients that reached 0.996 demonstrate the feasibility, suitability and adequacy of these techniques to address the auto essay grading problem. We believe that further investigations to other samples of datasets in different fields are required before claiming that.

Online publication date: Mon, 30-Dec-2013

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