Learning Syllogistic by Training Computers
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Published:
2020-10-15
How to Cite
Learning Syllogistic by Training Computers. (2020). Polisemia, 16(30), 04-23. https://doi.org/10.26620/uniminuto.polisemia.16.30.2020.04-23
Article Details
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Main Article Content
Abstract
Inspired by the mechanisms and results of the learning by teaching method (LdL, German abreviation for Lernen durch Lehren), in this paper we advance a didactic model to learn logic by teaching logic. In particular, we propose a model to learn syllogistic by training an artificial neural network. At the end we show a qualitative report of two teaching experiences using
the proposed model.
Keywords:
LdL didactic model, learning by teaching, automatic learning, artificial neural network, syllogistic
References
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Roscoe, R. D. y Chi, M. T. H. (2008). Tutor learning: the role of explaining and responding to questions. Instructional Science, 36(4), 321-350.
Rosenblatt, F. (1957). The Perceptron -a perceiving and recognizing automaton (Report 85-460-1). Cornell Aeronautical Laboratory.
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Russell, S. y Norvig, P. (1995). Artificial Intelligence. A modern approach. Nueva Jersey: Prentice Hall.
Safiye, A. (2015). Is learning by teaching effective in gaining 21st century skills? The views of pre-service science teachers. Educational Sciences: Theory &
Practice, 15(6), 1441-1457. https://doi.org/10.12738/estp.2016.1.0019
Sommers, F. (1984). The logic of natural language. Oxford University Press.
Brooks, R. A. (1999). Cambrian intelligence: the early history of the new AI.MIT Press.
Campirán, A. (2005). Autobservación y metacognición. Ergo. Colección Temas Selectos, (1), 91-106.
Cohen, P. A., Kulik, J. A. y Kulik, C. C. (1982). Educational outcomes of tutoring: A meta‐analysis of findings. American Educational Research Journal, 19(2), 237-248. https://doi.org/10.3102/00028312019002237
Gallier, J. H. (2015). Logic for computer science: foundations of automatic theorem proving (2.a ed.). Courier Dover.
Haugeland, J. (1989). Artificial intelligence: the very idea. MIT Press.
Huth, M. y Ryan, M. (2004). Logic in computer science: modelling and reasoning about systems. Cambridge University Press.
Koh, A. W. L, Lee, S.C. y Lim, S. W. H (2018). The learning benefits of teaching: A retrieval practice hypothesis. Applied Cognitive Psychology 32, 401– 410.
https://doi.org/10.1002/acp.3410
Martin, J. y Kelchner, R. (1998). Lernen durch lehren. http://www.lernendurch-lehren.de/Material/Publikationen/timm.pdf
McCulloch, W. y Pitts, W. (1943). A logical calculus of the ideas immanent in nervous activity. Bulletin of Mathematical Biophysics, 5, 115-133. https://doi.org/10.1007/BF02478259
Minsky M. L. y Papert S. A. (1969). Perceptrons. MIT Press.
Moss, L. (2015). Natural Logic. En S. Lappin & C. Fox (Eds.), The handbook of contemporary semantic theory. John Wiley & Sons.
Roscoe, R. D. y Chi, M. T. H. (2008). Tutor learning: the role of explaining and responding to questions. Instructional Science, 36(4), 321-350.
Rosenblatt, F. (1957). The Perceptron -a perceiving and recognizing automaton (Report 85-460-1). Cornell Aeronautical Laboratory.
Rumelhart, D. E., Hinton, G. E. y Williams, R. J. (1986). Learning representations by back-propagating errors. Nature, 323(6088), 533-536.
Russell, S. y Norvig, P. (1995). Artificial Intelligence. A modern approach. Nueva Jersey: Prentice Hall.
Safiye, A. (2015). Is learning by teaching effective in gaining 21st century skills? The views of pre-service science teachers. Educational Sciences: Theory &
Practice, 15(6), 1441-1457. https://doi.org/10.12738/estp.2016.1.0019
Sommers, F. (1984). The logic of natural language. Oxford University Press.
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