Abstract
Argument-based machine learning (ABML) knowledge refinement loop offers a powerful knowledge elicitation tool, suitable for obtaining expert knowledge in difficult domains. In this paper, we first use it to conceptualize a difficult, even ill-defined concept: distinguishing between “basic” and “advanced” programming style in python programming language, and then to teach this concept in an interactive learning session between a student and the computer. We demonstrate that by automatically selecting relevant examples and counter examples to be explained by the student, the ABML knowledge refinement loop provides a valuable interactive teaching tool.
Keywords
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Ala-Mutka, K., Uimonen, T., Järvinen, H.M.: Supporting students in C++ programming courses with automatic program style assessment. JITE 3, 245–262 (2004)
Groznik, V., Guid, M., Sadikov, A., Možina, M., Georgiev, D., Kragelj, V., Ribarič, S., Pirtošek, Z., Bratko, I.: Elicitation of neurological knowledge with argument-based machine learning. Artificial Intelligence in Medicine 57(2), 133–144 (2013)
Guid, M., Možina, M., Groznik, V., Georgiev, D., Sadikov, A., Pirtošek, Z., Bratko, I.: 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)
Li, N., Tian, Y., Cohen, W.W., Koedinger, K.R.: Integrating perceptual learning with external world knowledge in a simulated student. In: Lane, H.C., Yacef, K., Mostow, J., Pavlik, P. (eds.) AIED 2013. LNCS (LNAI), vol. 7926, pp. 400–410. Springer, Heidelberg (2013)
Matsuda, N., Keiser, V., Raizada, R., Tu, A., Stylianides, G., Cohen, W.W., Koedinger, K.R.: Learning by teaching simStudent: Technical accomplishments and an initial use with students. In: Aleven, V., Kay, J., Mostow, J. (eds.) ITS 2010, Part I. LNCS, vol. 6094, pp. 317–326. Springer, Heidelberg (2010)
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), Patras, Greece, pp. 234–238 (2008)
Možina, M., Žabkar, J., Bratko, I.: Argument based machine learning. Artificial Intelligence 171(10/15), 922–937 (2007)
O’Keefe, R.: The Craft of Prolog. The MIT Press (2009)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Zapušek, M., Možina, M., Bratko, I., Rugelj, J., Guid, M. (2014). Designing an Interactive Teaching Tool with ABML Knowledge Refinement Loop. In: Trausan-Matu, S., Boyer, K.E., Crosby, M., Panourgia, K. (eds) Intelligent Tutoring Systems. ITS 2014. Lecture Notes in Computer Science, vol 8474. Springer, Cham. https://doi.org/10.1007/978-3-319-07221-0_73
Download citation
DOI: https://doi.org/10.1007/978-3-319-07221-0_73
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-07220-3
Online ISBN: 978-3-319-07221-0
eBook Packages: Computer ScienceComputer Science (R0)