Feature Selection for Bloom’s Question Classification in Thai Language

  • Khantharat AnekboonEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 858)


Bloom’s taxonomy cognitive domain is a list of knowledge and the development of intellectual skills words. It is widely used in an assessment. Currently, in Thai language, teacher identifies Bloom’s taxonomy cognitive level manually, which is a tedious and a time-consuming task. This study presents automatic natural language question classification in Thai, feature selection is focused. Several previous works have been studied to fulfill Bloom’s taxonomy cognitive domain; however, those works cannot apply to Thai language due to the language characteristic. This study shows that verb, adverb, adjective, conjunction, and question tag should be selected as features in Thai’s exam classification. The dataset has been collected from a number of websites on Bloom’s cognitive domain literature. Each question is processed through cleaning data, word segmentation, part-of-speech tagging, and feature selection. After that selected feature, 70% of data set is used for training into a model. Four different classifier models, namely, Naïve Bayes, decision tree, multilayer perceptron, and support vector machine are used to show the effects of the proposed feature selection technique. The results from the testing data (30% of data set) show that the proposed technique with support vector machine gives the good value of accuracy, precision, and recall, which is 71.2%, 72.2%, and 71.2%, respectively.


Feature selection Question classification Bloom’s cognitive domain Thai language Natural language processing 


  1. 1.
    The Glossary of Education Reform (2017).
  2. 2.
    Omara, N., et al.: Automated analysis of exam questions according to Bloom’s taxonomy. Procedia Soc. Behav. Sci. 59, 297–303 (2012)CrossRefGoogle Scholar
  3. 3.
    Nayef, E.G., Rosila, N., Yaacob, N., Ismail, H.N.: Taxonomies of educational objective domain. Int. J. Acad. Res. Bus. Soc. Sci. 3(9), 2222–6990 (2013)Google Scholar
  4. 4.
    Chang, W., Chung, M.: Automatic applying Bloom’s taxonomy to classify and analysis the cognition level of English question items. In: Pervasive Computing (JCPC), pp. 727–733 (2009)Google Scholar
  5. 5.
    Haris, S.S., Omar, N.: Determining cognitive category of programming question with rule-based approach. Int. J. Inf. Process. Manag. 4(3), 86–95 (2013)Google Scholar
  6. 6.
    Jayakodi, K., Bandara, M., Perera, I., Meedeniya, D.: WordNet and cosine similarity based classifier of exam questions using Bloom’s taxonomy. Int. J. Emerg. Technol. Learn. 11(4), 142–149 (2016)CrossRefGoogle Scholar
  7. 7.
    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 (ISDA), pp. 866–870 (2010)Google Scholar
  8. 8.
    Haris, S.S., Omar, N.: Bloom’s taxonomy question categorization using rules and N-gram approach. J. Theor. Appl. Inf. Technol. 76(3), 401–407 (2015)Google Scholar
  9. 9.
    Pincay, J., Ochoa, X.: Automatic classification of answers to discussion forums according to the cognitive domain of Bloom’s taxonomy using text mining and a Bayesian classifier. In: EdMedia: World Conference on Educational Media and Technology, Canada, pp. 626–634 (2013)Google Scholar
  10. 10.
    Sangodiah, A., Ahmad, R., Fatimah, W., Ahmad, W.: A review in feature extraction approach in question classification using support vector machine. In: IEEE International Conference on Control System, Computing and Engineering, pp. 536–541 (2014)Google Scholar
  11. 11.
    Osman, A., Yahaya, A.A.: Classifications of exam questions using linguistically-motivated features: a case study based on Bloom’s taxonomy. In: The Sixth International Arab Conference on Quality Assurance in Higher Education, Saudi Arabia, pp. 467–474 (2016)Google Scholar
  12. 12.
    Yusof, N., Chai, J.H.: Determination of Bloom’s cognitive level of question items using artificial neural network. In: 10th International Conference on Intelligent Systems Design and Applications, pp. 866–870 (2010)Google Scholar
  13. 13.
    Supriyanto, C., Yusof, N., Nurhadiono, B.: Two-level feature selection for Naive Bayes with kernel density estimation in question classification based on Bloom’s cognitive levels. In: Information Technology and Electrical Engineering (ICITEE) International Conference, pp. 237–241 (2013)Google Scholar
  14. 14.
    Bloom, B.S., Engelhart, M.D., Furst, E.J., Hill, W.H., Krathwohl, D.R.: Taxonomy of Educational Objectives, Handbook I: The Cognitive Domain. David McKay Co Inc., New York (1956)Google Scholar
  15. 15.
  16. 16.
    Hall, M., et al.: The WEKA data mining software: an update. SIGKDD Explor. 11(1), 10–18 (2009)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of Computer and Information ScienceKMUTNBBangkokThailand

Personalised recommendations