EXAM QUESTIONS CLASSIFICATION BASED ON BLOOM'S TAXONOMY COGNITIVE LEVEL USING CLASSIFIERS COMBINATION

• Published in: Journal of Theoretical and Applied Information Technology 2015-08, Vol.78 (3), p.447-447
• Main Authors: Abduljabbar, Dhuha Abdulhadi, Omar, Nazlia
• Format: Article
• Language: English
• Subjects: Algorithms; Classification; Classifiers; Information technology; Strategy; Support vector machines; Taxonomy; Test procedures
• ISSN: 1817-3195

Assessment through written examination is a traditional method but it is a universal test method practiced in most of the educational institutions today. 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), Nai'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.