Journal of Engineering Science and Technology


Bloom’s taxonomy has been proposed for categorizing examination questions in accordance with the student’s cognitive ability. Recently, researchers tend to utilize machine learning techniques in order to classify the questions. However, there is still a remarkable limitation, which can be represented by the ambiguity lies on the question. Due to the short length of the question, it is difficult to identify the contextual information of the words. This means that a single word could yield multiple meanings. This would significantly affect the process of classification especially for the verbs that are usually located within the question such as ‘define’ or ‘write’. The ambiguity of such verbs would mislead the classification process regarding Bloom’s cognitive levels. Therefore, this study aims to propose a combination method of semantic and syntactic approaches in order to overcome such drawback. The semantic approach aims to utilize an external knowledge source in order to retrieve semantic correspondences. Whereas, the syntactic approach aims to determine the syntactic tag of the terms to address the significant verbs and nouns. Finally, three machine learning techniques will be used including Support Vector Machine, J48 and Naïve Bayes classifiers to classify the questions. In order to assess the effectiveness of the proposed combination method, the classifiers have been applied with the proposed combination and without it. Results revealed that the classifiers with the combination method have outperformed the traditional ones. This implies the significance of using the proposed semantic and syntactic approaches.