An Enhanced Automatic Arabic Essay Scoring System Based on Machine Learning Algorithms

Despite the extensive effort to improve intelligent educational tools for smart learning environments, automatic Arabic essay scoring remains a big research challenge. The nature of the writing style of the Arabic language makes the problem even more complicated. This study designs, implements, and evaluates an automatic Arabic essay scoring system. The proposed system starts with pre-processing the student answer and model answer dataset using data cleaning and natural language processing tasks. Then, it comprises two main components: the grading engine and the adaptive fusion engine. The grading engine employs string-based and corpus-based similarity algorithms separately. After that, the adaptive fusion engine aims to prepare students' scores to be delivered to different feature selection algorithms, such as Recursive Feature Elimination and Boruta. Then, some machine learning algorithms such as Decision Tree, Random Forest, Adaboost, Lasso, Bagging, and K-Nearest Neighbor are employed to improve the suggested system's efficiency. The experimental results in the grading engine showed that Extracting DIStributionally similar words using the CO-occurrences similarity measure achieved the best correlation values. Furthermore, in the adaptive fusion engine, the Random Forest algorithm outperforms all other machine learning algorithms using the (80%-20%) splitting method on the original dataset. It achieves 91.30%, 94.20%, 0.023, 0.106, and 0.153 in terms of Pearson's Correlation Coefficient, Willmot's Index of Agreement, Mean Square Error, Mean Absolute Error, and Root Mean Square Error metrics, respectively.