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Assessing Structured Examination Question Using Automated Keyword Expansion Approach

  • Rayner AlfredEmail author
  • Kay Lie Chan
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 937)

Abstract

Course assessment through written examination is the most common approach used to access student’s learning curve in educational institutions today. In order to fulfill the learning objective, the examination question must be provided in accordance with the subject content learned by students. However, the process of preparing the examination questions is very challenging for most lecturers. The situation is getting more challenging when lecturers try to prepare reasonable and good quality questions that assess different capabilities and students’ cognitive levels. Thus, the Bloom’s Taxonomy has become a common reference for the learning and teaching process used as a guide for the production of exam questions. This paper proposes an automated assessment of structured examination questions using keywords expansion approach in order to determine the appropriate category based on Bloom taxonomy. This system focuses on applying the Revised Bloom’s Taxonomy that fits well for computer science subject in order to categorize the level of difficulties for each examination question. A keyword expansion and WordNet have been integrated in this system in order to handle and find the nearest synonyms for the unknown keywords that exist in the examination question. Based on the test results obtained, the average percentage of correctly classified questions is 48.14% while the average percentage of misclassified questions is 51.86%. These results indicate that the results of evaluating examination papers manually are less accurate based on the results of evaluating examination papers generated using the proposed system.

Keywords

Revised Bloom’s taxonomy WordNet Keyword expansion Synonyms 

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Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.Knowledge Technology Research Unit, Faculty of Computing and InformaticsUniversiti Malaysia SabahKota KinabaluMalaysia

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