A Comparative Analysis of Euclidean, Jaccard and Cosine Similarity Measure and Arabic Wordnet for Automated Arabic Essay Scoring

Alobed, M.; Altrad, A. M. M.; Bakar, Z. B. A.

The Coronavirus crisis has cast a shadow over the education sector;As it pushed schools, universities, and educational institutions to close their doors, to reduce the chances of its spread. This raised great concern among those affiliated with this sector, especially students preparing to take important exams. All this pushed educational institutions to switch to E-learning, as an alternative that has long been talked about over the need to integrate it into the educational process. From this standpoint, the importance of electronic exams as an alternative to the traditional paper exams appears, there are many assessment methods in E-learning, one of these assessments is the essay. The study used a variety of similarity tests and used an Arabic data set of 40 student answers. The results indicate that the Arabic Automated Essay System will improve with Cosine similarity and Arabic WordNet. Automatic Arabic Essay Scoring using WordNet is higher in terms of precision than the Automated Arabic Essay Scoring without using WordNet dependent on the mean absolute error value and Pearson correlation. The results clearly show the Cosine similarity with Arabic WordNet has the lowest error. Cosine similarity with all stemming types has the lowest error compared with the Jaccard and Euclidean similarity. Euclidean similarity has the highest error. © 2021 IEEE.