期刊名称：Journal of Theoretical and Applied Information Technology
印刷版ISSN：1992-8645
电子版ISSN：1817-3195
出版年度：2015
卷号：78
期号：3
出版社：Journal of Theoretical and Applied
摘要：Assessment through written examination is a traditional method but it is a universal test method practiced in most of the educational institutions today. Therefore, the question must be provided in accordance with the subject content learned by students to fulfil learning objectives. However, the process of questions writing is very challenging step for the lecturer. The situation is getting more challenging when lecturers try to produce good quality and fair questions to assess different level of cognitive. Thus, the Bloom�s Taxonomy has become a common reference for the teaching and learning process used as a guide for the production of exam questions. Exam questions classification presents a particular challenge is the classification of short text questions due to short text involves text with less than 200 characters. In addition, the features of short text are very sparse and far. This study proposed a new method to classify exam questions automatically according to the cognitive levels of Bloom�s taxonomy by implementing a combination strategy based on voting algorithm that combines three machine learning classifiers. In this work, several classifiers are taken into consideration. The classifiers are, Support Vector Machine (SVM), Na�ve Bayes (NB), and k-Nearest Neighbour (k-NN) that are used to classify the question with or without feature selection methods, namely Chi-Square, Mutual Information and Odd Ratio. Then a combination algorithm is used to integrate the overall strength of the three classifiers (SVM, NB, and k-NN). The classification model achieves highest result through the combination strategy by applying Mutual Information, which proved to be promising and comparable to other similar models. These experiments aimed to efficiently integrate different feature selection methods and classification algorithms to synthesize a classification procedure more accurately.
