Integration of machine learning approach in item bank test system

Item test bank system plays very important role in auto generating test or exam paper in assessments in schools and universities. A quite number of researchers have proposed some algorithms in generating test paper based on some well-defined attributes such as time, question type, knowledge point, difficulty level and others. It has always been the aim of these researchers to generate high quality test paper with appropriate level of difficulty in test questions. As a result of this, Bloom taxonomy has been adopted to ensure difficulty level of test questions is appropriate. However, there is no evidence that current test items or questions in the item test bank system are classified in accordance to BT using machine learning approach. Manual classifying is tedious and laborious work and inconsistency in classifying items can take place due to different judgement from instructors. A better approach is to use machine learning namely question classifier such as Support Vector Machine to automate the classification of the test items. Despite some research work has been done on using classifiers to classify questions, there is no evidence that this type of work has been integrated into item bank test system. In view of this, this study proposes a change in existing framework of item test bank system by integrating the facility to automate classifying items in accordance to Bloom taxonomy. With all this in place, the automation of classifying questions or test items in accordance to BT with a reasonable accuracy can be achieved. © 2016 IEEE.