Bloom’s Taxonomy–Based Classification for Item Bank Questions Using Support Vector Machines

Part of the Studies in Computational Intelligence book series (SCI, volume 431)

Abstract

This paper investigates the effectiveness of support vector machines for the classification of item bank question into Bloom’s taxonomy cognitive levels. In doing so, a dataset of pre-classified questions has been collected. Each question has been processed through removal of punctuations, tokenization, stemming, term weighting, and length normalization. Using this dataset, the performance of support vector machines has been evaluated considering the effect of term frequency and stopwords removal. The results show a satisfactory performance of support vector machines, which declines as the frequency of the terms used to represent question increases. The best performance is obtained when term frequency is greater than or equal to two. Moreover, the results show that the removal of stopwords does not improve the performance significantly.

Keywords

Support Vector Machine Term Frequency Concept Drift Item Question Question Classification 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Bloom, B.S.: Taxonomy of educational objectives. Handbook I. The Cognitive Domain. David McKay Co. Inc., New York (1956)Google Scholar
  2. 2.
    Bullington, J., Endres, I., Rahman, M.: Open ended question classification using support vector machines. In: MAICS (2007)Google Scholar
  3. 3.
    Joachims, T.: Text Categorization with Support Vector Machines: Learning with Many Relevant Features. In: Nédellec, C., Rouveirol, C. (eds.) ECML 1998. LNCS, vol. 1398, pp. 137–142. Springer, Heidelberg (1998)CrossRefGoogle Scholar
  4. 4.
    Jung-Lung, H.: A text mining approach for formative assessment in e-learning environment. Dissertation, National Sun Yat-sen University, Taiwan (2008)Google Scholar
  5. 5.
    Karahoca, A., Karahoca, D., Ince, F.I., Gökçeli, R., Aydin, N., Güngör, A.: Intelligent question classification for e-learning by ANFIS. In: E-learning Conference 2007, pp. 156–159 (2007)Google Scholar
  6. 6.
    Klinkenberg, R., Joachims, T.: Detecting concept drift with support vector machines. In: Proc. of the 17th Int. Conf. on Machine Learning, ICML 2000, Stanford, CA, pp. 487–494 (2000)Google Scholar
  7. 7.
    Nuntiyagul, A., Naruedomkul, K., Cercone, N., Wongsawang, D.: Adaptable learning assistant for item bank management. Computers & Education 50, 357–370 (2008)CrossRefGoogle Scholar
  8. 8.
    Porter, M.F.: An algorithm for suffix stripping. Program 14(3), 130–137 (1980)CrossRefGoogle Scholar
  9. 9.
    Taira, H., Haruno, M.: Feature selection in SVM text categorization. In: Proc. of the 16th Conf. of American Association for Artificial Intelligence, AAAI 1999, Orlando, FL, pp. 480–486 (1999)Google Scholar
  10. 10.
    Wiggins, G.: Educative assessment designing assessments to inform and improve student performance. Jossey-Bass Inc., California (2003)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  1. 1.Faculty of Computer Science and Information SystemsNajran UniversityNajranKingdom of Saudi Arabia

Personalised recommendations