Affiliations: Computer Research Institute, King Abdulaziz City for Science and Technology, Riaydh, Saudi Arabia. E-mails: {mghamdi, alkanhal, badrashiny, qabbany, aareshey, asnharbi}@kacst.edu.sa
Abstract: Essay writing is widely used for student performance assessment. This paper presents a hybrid automatic essay scoring system (AES) for Arabic essays. The system attempts at saving the time teachers spend on reading and scoring Arabic essays. It utilizes latent semantic analysis (LSA) and three linguistic features (i.e., word stemming, number of words and number of spelling mistakes). This paper also describes an algorithm to determine the optimal reduced dimensionality used in LSA. To evaluate the performance of this system, an Arabic dataset was developed based on essays collected from college students. The experimental results show the effectiveness of using LSA for scoring Arabic essays, especially when combined with other linguistic features. The system shows that 96.72% of the test data are correctly scored and the correlation between automatic and manual scores is 0.78, which is close to the interhuman correlation of 0.7.
