Automatic Essay Scoring for Arabic Short Answer Questions using Text Mining Techniques

Automated Essay Scoring (AES) systems involve using a specially designed computing program to mark students’ essays. It is a form of online assessment supported by natural language processing (NLP). These systems seek to exploit advanced technologies to reduce the time and effort spent on the exam scoring process. These systems have been applied in several languages, including Arabic. Nevertheless, the applicable NLP techniques in Arabic AES are still limited, and further investigation is needed to make NLP suitable for Arabic to achieve human-like scoring accuracy. Therefore, this comparative empirical experimental study tested two word-embedding deep learning approaches, namely BERT and Word2vec, along with a knowledge-based similarity approach; Arabic WordNet. The study used the Cosine similarity measure to provide optimal student answer scores. Several experiments were conducted for each of the proposed approaches on two available Arabic short answer question datasets to explore the effect of the stemming level. The quantitative results of this study indicated that advanced models of contextual embedding can improve the efficiency of Arabic AES as the meaning of words can differ in the different contexts. Therefore, serve as a catalyst for future research based on contextual embedding models, as the BERT approach achieved the best Pearson Correlation (.84) and RMSE (1.003). However, this research area needs further investigation to increase the accuracy of Arabic AES to become a practical online scoring system.

@article{Meccawy2023,
  title     = {Automatic Essay Scoring for Arabic Short Answer Questions using Text Mining Techniques},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {14},
  number    = {6},
  year      = {2023},
  publisher = {The Science and Information Organization},
  author    = {Maram Meccawy and Afnan Ali Bayazed and Bashayer Al-Abdullah and Hind Algamdi},
  doi       = {10.14569/IJACSA.2023.0140682},
  url       = {https://doi.org/10.14569/IJACSA.2023.0140682}
}