Arabic Questions Classification Using Modified TF-IDF

Classifying the cognitive levels of assessment questions according to Bloom's taxonomy can help instructors design effective assessments that are well aligned with the intended learning outcomes. However, the classification process is time consuming and requires experience. Many studies have attempted to automate the process by utilizing different machine learning and text mining approaches, but none has examined the classification of Arabic questions. The purpose of this study is to examine this research gap and to introduce a new feature extraction method that would better suit Arabic questions and their unique characteristics. It also aims to provide Arab instructors with a tool that can help them automatically classify their assessment questions. To accomplish this purpose, the study developed a dataset of more than 600 Arabic assessment questions. It then proposed a modified term frequency-inverse document frequency (TF-IDF) method for extracting features from Arabic questions. Unlike the traditional TF-IDF, the proposed method was designed to take the nature of assessment questions into consideration. It was evaluated by comparing it to two methods that have been used for classifying English questions, i.e., the traditional TF-IDF and a modified TF-IDF method called term frequency part-of-speech-inverse document frequency (TFPOS-IDF). A t-test was utilized to examine whether the difference in performance between the three methods was statistically significant. The proposed method outperformed the two other methods. The overall accuracy, precision, and recall scored by the proposed method were significantly higher than those scored by the traditional TF-IDF and TFPOS-IDF methods. The evaluation results indicate the promising potential of the proposed method, which can be extended to other languages.