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)


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.


Revised Bloom’s taxonomy WordNet Keyword expansion Synonyms 


  1. 1.
    Yusof, N., Hui, C.J.: Determination of Bloom’s cognitive level of question items using artificial neural network. In: 10th International Conference on Intelligent Systems Design and Applications, Cairo, pp. 866–870 (2010)Google Scholar
  2. 2.
    Ahmad, N.D., Adnan, W.A.W., Aziz, M.A., Yusof, M.Y.: Automating preparation of exam questions: exam question classification system (EQCS). In: 2011 International Conference on Research and Innovation, pp. 1–6 (2011)Google Scholar
  3. 3.
    Raus M.I.M., Janor, R.M., Sadjirin, R., Sahri, Z.: The development of i-QuBES for UiTM: from feasibility study to the design phase. In: 2014 IEEE 5th Control and System Graduate Research Colloquium, Shah Alam, Malaysia, pp. 96–101. UiTM(2014)Google Scholar
  4. 4.
    Pôssas, B., Ziviani, N., Meira Jr. W., Ribeiro-Neto, B.: Set-based model: a new approach for information retrieval. In: Proceedings of the 25th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2002), pp. 230–237. ACM, New York (2002)Google Scholar
  5. 5.
    Chang, W.C., Chung, M.S.: Automatic applying Bloom’s taxonomy to classify and analysis the cognition level of English question items. In: 2009 Joint Conferences on Pervasive Computing (JCPC), Tamsui, Taipei, pp. 727–734 (2009)Google Scholar
  6. 6.
    Salmah, F., Siti Hasnah, T., Asni, T.: Assessing perceptions of academic staff in using SmartUMS for teaching and learning. Int. J. E-Learn. Pract. (IJELP) 1(1), 60–67 (2014)Google Scholar
  7. 7.
    Krizhanovsky, A.A., Lin, F.: Related terms search based on WordNet/Wiktionary and its application in Ontology Matching. Accessed 10 Aug 2018
  8. 8.
    Li, H., Tian, Y., Ye, B., Cai, Q.: Comparison of current semantic similarity methods in WordNet. Paper presented at 2010 International Conference on Computer Application and System Modeling (ICCASM 2010), pp. 408–411 (2010)Google Scholar
  9. 9.
    Harshala, M.D., Chaitali, S.B., Minal, A.M., Minal, Y.P.: Language translator for deaf community. Int. Res. J. Eng. Technol. (IRJET) 3(4), 848–852 (2016)Google Scholar
  10. 10.
    Vikram, S., Balwinder, S.: An effective tokenization algorithm for information retrieval systems. Int. J. Database Manag. Syst. (IJDMS) 6(6), 13–24 (2014)CrossRefGoogle Scholar
  11. 11.
    Quillian, M.: Semantic Memory. In: Minsky, M. (ed.) Semantic Information Processing, pp. 227–270. MIT Press, Cambridge (1968)Google Scholar

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© 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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