Automatic Short Answer Grading

Abstract

Much research has been done on automatic grading of student answers since 1966, and it was divided into short answer grading and essay scoring. Our main target is on the short answer-grading task. Therefore, we have implemented two modules an Ensemble-model that is based on similarity algorithms and Neural Network module using sentence embedding pre-trained models. In the first module, we are implementing some text similarity algorithms on Texas dataset. These text similarity algorithms are classified into string similarity using Abydos package that contains 168 string similarity algorithms, semantic similarity (corpus and knowledge-based) and different deep learning embedding models similarity (transformers). Different experiments were done by testing them separately and combining them too, to propose our new model and methodology to achieve the maximum correlation that can be produced from this task using this module which was 65.14%. The neural network module, which used the T5 sentence embedding pre-trained model, reached 92.80 % correlation score, which is significantly better than the other module and had the greatest correlation result when compared to other studies.