Title: Soft computing auto essay grading

Authors: Labib Arafeh

Addresses: Computer Engineering Department, Faculty of Engineering, Al-Quds University, Main Campus, Abu Dis, P.O. Box 20002, Palestine

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.

Keywords: automated essay grading; AEG; soft computing; fuzzy logic; neuro fuzzy modelling; artificial neural networks; ANNs; knowledge extraction; keyword identification; synonyms; essays; automated essay marking.

DOI: 10.1504/IJCEELL.2013.054289

International Journal of Continuing Engineering Education and Life-Long Learning, 2013 Vol.23 No.2, pp.147 - 166

Received: 08 May 2021
Accepted: 12 May 2021

Published online: 02 Jun 2013 *

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