Automatic Labeling of Forums Using Bloom’s Taxonomy

  • Vanessa Echeverría
  • Juan Carlos Gomez
  • Marie-Francine Moens
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8346)


The labeling of discussion forums using the cognitive levels of Bloom’s taxonomy is a time-consuming and very expensive task due to the big amount of information that needs to be labeled and the need of an expert in the educational field for applying the taxonomy according to the messages of the forums. In this paper we present a framework in order to automatically label messages from discussion forums using the categories of Bloom’s taxonomy. Several models were created using three kind of machine learning approaches: linear, Rule-Based and combined classifiers. The models are evaluated using the accuracy, the F1-measure and the area under the ROC curve. Additionally, a statistical significance of the results is performed using a McNemar test in order to validate them. The results show that the combination of a linear classifier with a Rule-Based classifier yields very good and promising results for this difficult task.


CSCL Bloom’s taxonomy logistic regression classifier Rule-Based classifier combined classifiers 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Bloom, B.S., Engelhart, M., Furst, E.J., Hill, W.H., Krathwohl, D.R.: Taxonomy of Educational Objectives: The Classification of Educational Goals. In: Handbook I: Cognitive Domain, vol. 19, Longman, Green (1956)Google Scholar
  2. 2.
    Chang, W., Chung, M.: Automatic applying Bloom’s taxonomy to classify and analysis the cognition level of English question items. In: 2009 Joint Conferences on Pervasive Computing (JCPC), pp. 727–734 (2009)Google Scholar
  3. 3.
    Chiluiza, K., Echeverria, V.: Cognitive and Meta-Cognitive Skills Measurement: What about the Task in Web 2.0 Environments? In: Proceedings of Society for Information Technology & Teacher Education International Conference, pp. 1685–1690 (2012)Google Scholar
  4. 4.
    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
  5. 5.
    Jolliffe, I.: Principal Component Analysis. Wiley Online Library (2005)Google Scholar
  6. 6.
    Kittler, J.: Combining classifiers: A theoretical framework. Pattern Analysis and Applications 1(1), 18–27 (1998)CrossRefMathSciNetGoogle Scholar
  7. 7.
    Krathwohl, D.: A revision of Blooms Taxonomy: An overview. Theory into Practice 41(4), 212–218 (2002)CrossRefGoogle Scholar
  8. 8.
    Miyake, N.: Computer supported collaborative learning. In: The SAGE Handbook of E-Learning Research, pp. 248–267. SAGE Publications Ltd. (2007)Google Scholar
  9. 9.
    Omar, N., Haris, S.S., Hassan, R., Arshad, H., Rahmat, M., Zainal, N.F., Zulkifli, R.: Automated Analysis of Exam Questions According to Bloom’s Taxonomy. Procedia - Social and Behavioral Sciences 59, 297–303 (2012)CrossRefGoogle Scholar
  10. 10.
    Pincay, J., Ochoa, X.: Automatic Classification of Answers to Discussion Forums According to the Cognitive Domain of Blooms Taxonomy using Text Mining and a Bayesian Classifier. In: Proceedings of World Conference on Educational Multimedia, Hypermedia and Telecommunications, pp. 626–634 (2013)Google Scholar
  11. 11.
    Salton, G., Buckley, C.: Term-weighting approaches in automatic text retrieval. Information Processing & Management 24(5), 513–523 (1988)CrossRefGoogle Scholar
  12. 12.
    Sebastiani, F.: Machine learning in automated text categorization. ACM Computing Surveys 34(1), 1–47 (2002)CrossRefGoogle Scholar
  13. 13.
    Valcke, M., Wever, B.D., Zhu, C., Deed, C.: Supporting active cognitive processing in collaborative groups: The potential of Blooms taxonomy as a labeling tool. The Internet and Higher Education 12(3), 165–172 (2009)CrossRefGoogle Scholar
  14. 14.
    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
  15. 15.
    Zurita, G., Nussbaum, M.: Computer supported collaborative learning using wirelessly interconnected handheld computers. Computers & Education 42(3), 289–314 (2004)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Vanessa Echeverría
    • 1
  • Juan Carlos Gomez
    • 2
  • Marie-Francine Moens
    • 2
  1. 1.Centro de Tecnologías de InformaciónEscuela Superior Politécnica del LitoralGuayaquilEcuador
  2. 2.Department of Computer ScienceKU LeuvenHeverleeBelgium

Personalised recommendations