Aprender silogística entrenando computadoras
Contenido principal del artículo
Resumen
Inspirados por los mecanismos y los resultados del método de aprendizaje mediante la enseñanza (LdL, siglas en alemán de Lernen durch Lehren), en este trabajo sugerimos un modelo didáctico para aprender lógica enseñando lógica. En particular, proponemos un modelo para aprender silogística entrenando una red neuronal artificial. Al final exponemos un reporte
cualitativo de dos experiencias docentes usando el modelo propuesto.
Citas
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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.