Advertisement

Learning by Arguing in Argument-Based Machine Learning Framework

  • Matej GuidEmail author
  • Martin Možina
  • Matevž Pavlič
  • Klemen Turšič
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11528)

Abstract

We propose an approach for the development of argument-based intelligent tutoring systems in which a domain that can be successfully addressed by supervised machine learning is taught in an interactive learning environment. The system is able to automatically select relevant examples and counter-examples to be explained by the students. The students learn by explaining specific examples, and the system provides automated feedback on students’ arguments, including generating hints. The role of an argument-based intelligent tutoring system is then to train the students to find the most relevant arguments. The students learn about the high-level domain concepts and then use them to argue about automatically selected examples. We demonstrate our approach in an online application that allows students to learn through arguments with the goal of improving their understanding of financial statements.

Keywords

Intelligent tutoring systems Learning by arguing Argument-based machine learning Automated feedback generation Financial statements 

References

  1. 1.
    Andriessen, J., Baker, M.: Arguing to learn. In: Sawyer, R.K. (ed.) The Cambridge Handbook of the Learning Sciences, chap. 22, pp. 439–460. Cambridge Handbooks in Psychology, Cambridge University Press (2014)Google Scholar
  2. 2.
    Andriessen, J., Baker, M., Suthers, D.D.: Arguing to learn: confronting cognitions in computer-supported collaborative learning environments, vol. 1. Springer, Heidelberg (2013).  https://doi.org/10.1007/978-94-017-0781-7CrossRefzbMATHGoogle Scholar
  3. 3.
    Bransford, J.D., Brown, A., Cocking, R.: How People Learn: Mind, Brain, Experience, and School. National Research Council, Washington (1999)Google Scholar
  4. 4.
    Chi, M.T., VanLehn, K.A.: The content of physics self-explanations. J. Learn. Sci. 1(1), 69–105 (1991)CrossRefGoogle Scholar
  5. 5.
    Clark, P., Boswell, R.: Rule induction with CN2: some recent improvements. In: Kodratoff, Y. (ed.) EWSL 1991. LNCS, vol. 482, pp. 151–163. Springer, Heidelberg (1991).  https://doi.org/10.1007/BFb0017011CrossRefGoogle Scholar
  6. 6.
    Ganguin, B., Bilardello, J.: Standard and Poor’s Fundamentals of Corporate Credit Analysis. McGraw-Hill Professional Publishing, New York (2004)Google Scholar
  7. 7.
    Groznik, V., et al.: Elicitation of neurological knowledge with argument-based machine learning. Artif. Intell. Med. 57(2), 133–144 (2013)CrossRefGoogle Scholar
  8. 8.
    Guid, M., et al.: ABML knowledge refinement loop: a case study. In: Chen, L., Felfernig, A., Liu, J., Raś, Z.W. (eds.) ISMIS 2012. LNCS (LNAI), vol. 7661, pp. 41–50. Springer, Heidelberg (2012).  https://doi.org/10.1007/978-3-642-34624-8_5CrossRefGoogle Scholar
  9. 9.
    Guid, M., Možina, M., Krivec, J., Sadikov, A., Bratko, I.: Learning positional features for annotating chess games: a case study. In: van den Herik, H.J., Xu, X., Ma, Z., Winands, M.H.M. (eds.) CG 2008. LNCS, vol. 5131, pp. 192–204. Springer, Heidelberg (2008).  https://doi.org/10.1007/978-3-540-87608-3_18CrossRefGoogle Scholar
  10. 10.
    Holt, R.: Financial Accounting: A Management Perspective. Ivy Learning Systems (2001)Google Scholar
  11. 11.
    Možina, M., Guid, M., Krivec, J., Sadikov, A., Bratko, I.: Fighting knowledge acquisition bottleneck with argument based machine learning. In: The 18th European Conference on Artificial Intelligence (ECAI), pp. 234–238. Patras, Greece (2008)Google Scholar
  12. 12.
    Možina, M., Žabkar, J., Bratko, I.: Argument based machine learning. Artif. Intell. 171(10/15), 922–937 (2007)MathSciNetCrossRefGoogle Scholar
  13. 13.
    Možina, M., Lazar, T., Bratko, I.: Identifying typical approaches and errors in prolog programming with argument-based machine learning. Expert Syst. Appl. 112, 110–124 (2018)CrossRefGoogle Scholar
  14. 14.
    Woolf, B.P.: Building Intelligent Interactive Tutors: Student-centered strategies for revolutionizing E-learning. Morgan Kaufmann Publishers Inc., San Francisco (2008)Google Scholar
  15. 15.
    Zapušek, M., Možina, M., Bratko, I., Rugelj, J., Guid, M.: Designing an interactive teaching tool with ABML knowledge refinement loop. In: Trausan-Matu, S., Boyer, K.E., Crosby, M., Panourgia, K. (eds.) ITS 2014. LNCS, vol. 8474, pp. 575–582. Springer, Cham (2014).  https://doi.org/10.1007/978-3-319-07221-0_73CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Matej Guid
    • 1
    Email author
  • Martin Možina
    • 1
  • Matevž Pavlič
    • 1
  • Klemen Turšič
    • 1
  1. 1.Faculty of Computer and Information ScienceUniversity of LjubljanaLjubljanaSlovenia

Personalised recommendations