THE USE OF CAUSAL MAPS AS INTERDISCIPLINARY DIDACTIC REDUCTION METHOD
Presented article is devoted to theoretical substantiation and development of interdisciplinary didactic reduction method, based on the use of causal maps. The article discusses properties of causal maps in the context of its use in teaching methods. It was determined that the creation of the causal maps that reflect all connections between and / or within the three information blocks should be an effective reduction method of learning content of the topics about measurements of the various sciences. The blocks are named: “The physical basis of measurement”, “The elements of the measuring instrument” and “The measurement technique”. The article establishes that the method of using of causal maps in related topics of natural sciences, engineering, social sciences and humanities corresponds to properties of didactic reduction methods, due to abstraction and providing the omission of factors that do not have a determining influence on the situation, and getting in the vertices concepts less volume than learning texts. The method can provide an illustrative or symbolic representation of complex information on causal maps, review situations based on known models. The research justifies the possibility of constructing of quantitative variables in formulas to their qualitative explanations and presentation of the relationship between them in the causal maps. The process of tasks’ decision, that has different difficulty levels, using causal maps allows to influence on additional perception channels, and through the students' understanding of causality allows intensifying the development of mental processes.
Forjan, M., & Slisko, J. (2014). Simplifications and Idealizations in High School Physics in Mechanics: A Study Of Slovenian Curriculum And Textbooks. European J of Physics Education, 5(3), 20-31. https://doi.org/10.20308/ejpe.68867
Forjan, M., & Slisko, J. (2014). Simplifications and idealizations in high school physics in thermodynamics, electricity and waves: A study of Slovenian textbooks. Latin-American Journal of Physics Education, 8(2), 241-247. Retrieved from http://lajpe.org/jun14/02_LAJPE_886%20Forjan-Slisko.pdf
Futschek, G. (2013). Extreme Didactic Reduction in Computational Thinking Education. X World Conference on Computers in Education. Retrieved from http://wcce2013.umk.pl/publications/Short_Papers/086-Futschek-SP-ext_msy.pdf
Costa, G., D’Ambrosio, C., & Martello, S. (2014). Graphsj 3: A modern didactic application for graph algorithms. Journal of Computer Science 10 (7), 1115-1119. https://doi.org/10.3844/jcssp.2014.1115.1119
Gopnik, A. & Schulz L. (Ed.). (2007). Causal Learning: Psychology, Philosophy, and Computation. New York, USA: Oxford University Press. https://doi.org/10.1093/acprof:oso/9780195176803.001.0001
Gopnik, A., Sobel, D. M., Glymour, C., Schulz, L. E., & Kushnir, T. (2004). A theory of causal learning in children: causal maps and Bayes nets. Psychological Review, 111(1), 3-32. https://doi.org/10.1037/0033-295X.111.1.3
Grüner, G. (1967). Die didaktische Reduktion als Kernstück der Didaktik [The didactic reduction as the core of didactics]. Die Deutsche Schule 7/8, 414-430.
Haolader, F. A., Ali, M. R., & Foysol, K. M. (2015). The Taxonomy for Learning, Teaching and Assessing: Current Practices at Polytechnics in Bangladesh and its Effects in Developing Students’ Competences. International Journal for Research in Vocational Education and Training, 2(2), 99-118. https://doi.org/10.13152/IJRVET.2.2.2
Lazarev, M. & Shmatkov, D. (2014). Metodyka navchannia neruinivnoho kontroliu maibutnikh inzheneriv-pedahohiv z vykorystanniam kauzal`nykh merezh [The methods of nondestructive testing teaching of the future engineers and pedagogues using causal networks]. Kharkiv, Ukraine: Tochka.
Lehner, M. (2012). Didaktische Reduktion [Didactic reduction]. Bern: Haupt.
Peña-Ayala, A., Sossa-Azuela, H., & Cervantes-Pérez, F. (2012). Predictive student model supported by fuzzy-causal knowledge and inference. Expert Systems with Applications, 39(5), 4690-4709. Retrieved from https://doi.org/10.1016/j.eswa.2011.09.086
Ruhm, K. H. (2011). From Verbal Models to Mathematical Models – A Didactical Concept not just in Metrology. Joint International IMEKO TC1+ TC7+ TC13 Symposium. Retrieved from http://www.iwf.mavt.ethz.ch/ConfiguratorJM/publications/From_Verba_132551321506614/IMEKOJenaRuhmII.pdf
Young, G. (2016). Causal Learning: Understanding the World. Unifying Causality and Psychology (pp. 387-415). Springer International Publishing. https://doi.org/10.1007/978-3-319-24094-7_16
- There are currently no refbacks.
Copyright (c) 2016 Daniyil Shmatkov
This work is licensed under a Creative Commons Attribution 4.0 International License.
ISSN 2410-8286 (Online), ISSN 2409-3351 (Print)