Posibles aportes del razonamiento analógico al problema de la abstracción y transferencia en la enseñanza de programación

En este artículo de revisión se explora una posible contribución de las investigaciones sobre razonamiento analógico al problema de la transferencia en programación -en la transición entre el aprendizaje de conceptos en la escuela media y su aplicación en la universidad. La facilidad con que los alu...

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Autores:
D’Angelo, Verónica
Tipo de recurso:
Article of investigation
Fecha de publicación:
2020
Institución:
Universidad Autónoma de Bucaramanga - UNAB
Repositorio:
Repositorio UNAB
Idioma:
spa
OAI Identifier:
oai:repository.unab.edu.co:20.500.12749/26437
Acceso en línea:
http://hdl.handle.net/20.500.12749/26437
https://doi.org/10.29375/25392115.4035
Palabra clave:
Programación de ordenadores
Razonamiento analógico
Abstracción
Transferencia
Computer programming
Analogical reasoning
Abstraction
Transfer
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dc.title.spa.fl_str_mv Posibles aportes del razonamiento analógico al problema de la abstracción y transferencia en la enseñanza de programación
dc.title.translated.eng.fl_str_mv Possible contributions of analogical reasoning to the problem of abstraction and transfer in programming teaching
title Posibles aportes del razonamiento analógico al problema de la abstracción y transferencia en la enseñanza de programación
spellingShingle Posibles aportes del razonamiento analógico al problema de la abstracción y transferencia en la enseñanza de programación
Programación de ordenadores
Razonamiento analógico
Abstracción
Transferencia
Computer programming
Analogical reasoning
Abstraction
Transfer
title_short Posibles aportes del razonamiento analógico al problema de la abstracción y transferencia en la enseñanza de programación
title_full Posibles aportes del razonamiento analógico al problema de la abstracción y transferencia en la enseñanza de programación
title_fullStr Posibles aportes del razonamiento analógico al problema de la abstracción y transferencia en la enseñanza de programación
title_full_unstemmed Posibles aportes del razonamiento analógico al problema de la abstracción y transferencia en la enseñanza de programación
title_sort Posibles aportes del razonamiento analógico al problema de la abstracción y transferencia en la enseñanza de programación
dc.creator.fl_str_mv D’Angelo, Verónica
dc.contributor.author.none.fl_str_mv D’Angelo, Verónica
dc.subject.spa.fl_str_mv Programación de ordenadores
Razonamiento analógico
Abstracción
Transferencia
topic Programación de ordenadores
Razonamiento analógico
Abstracción
Transferencia
Computer programming
Analogical reasoning
Abstraction
Transfer
dc.subject.keywords.eng.fl_str_mv Computer programming
Analogical reasoning
Abstraction
Transfer
description En este artículo de revisión se explora una posible contribución de las investigaciones sobre razonamiento analógico al problema de la transferencia en programación -en la transición entre el aprendizaje de conceptos en la escuela media y su aplicación en la universidad. La facilidad con que los alumnos construyen programas en entornos multimedia conlleva la desventaja de una dificultad para trasladar esos conceptos a los lenguajes “reales” basados en texto, probablemente porque no se ha trabajado suficiente en promover abstracciones en el nivel del problema. Según investigaciones en enseñanza de la programación, los alumnos suelen tener mayor dificultad en los niveles de abstracción superior (la comprensión del problema) que en los niveles inferiores (como la codificación). La comparación de problemas mediante razonamiento analógico es una estrategia proveniente de la psicología cognitiva extendida a diversas disciplinas. Sugerimos que su aplicación en el campo de la enseñanza de la programación podría contribuir a solucionar el problema de la dificultad de abstracción en el nivel del problema, y facilitar la transferencia.
publishDate 2020
dc.date.issued.none.fl_str_mv 2020-10-20
dc.date.accessioned.none.fl_str_mv 2024-09-09T21:14:39Z
dc.date.available.none.fl_str_mv 2024-09-09T21:14:39Z
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e-ISSN: 2539-2115
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dc.identifier.instname.spa.fl_str_mv instname:Universidad Autónoma de Bucaramanga UNAB
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instname:Universidad Autónoma de Bucaramanga UNAB
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https://doi.org/10.29375/25392115.4035
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dc.relation.references.none.fl_str_mv Armoni, M. (2013). On Teaching Abstraction in CS to Novices. Journal of Computers in Mathematics and Science Teaching, 32(3), 265–284. https://www.learntechlib.org/p/41271
Armoni, M., & Ben-Ari, M. (2013). Computer Science Concepts in Scratch. Department of Science Teaching , Weizmann Institute of Science. https://stwww1.weizmann.ac.il/scratch/scratch_en/
Armoni, M., Meerbaum-Salant, O., & Ben-Ari, M. (2015). From Scratch to “Real” Programming. ACM Transactions on Computing Education, 14(4), 1–15. https://doi.org/10.1145/2677087
Barnett, S. M., & Ceci, S. J. (2002). When and where do we apply what we learn?: A taxonomy for far transfer. Psychological Bulletin, 128(4), 612–637. https://doi.org/10.1037/0033-2909.128.4.612
Bell, T., Rosamond, F., & Casey, N. (2012). Computer Science Unplugged and Related Projects in Math and Computer Science Popularization. En H. L. Bodlaender, R. Downey, F. V. Fomin, & D. Marx (Eds.), The Multivariate
Algorithmic Revolution and Beyond. Lecture Notes in Computer Science, vol 7370 (pp. 398–456). Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-30891-8_18
Burgoon, E. M., Henderson, M. D., & Markman, A. B. (2013). There Are Many Ways to See the Forest for the Trees: A Tour Guide for Abstraction. Perspectives on Psychological Science, 8(5), 501–520. https://doi.org/10.1177/1745691613497964
Çakiroğlu, Ü., Sude, S., B., K., Sari, A., Yildiz, S., & Öztürk, M. (2018). Exploring perceived cognitive load in learning programming via Scratch. Research in Learning Technology, 26. https://doi.org/10.25304/rlt.v26.1888
Catrambone, R., & Holyoak, K. J. (1989). Overcoming contextual limitations on problem-solving transfer. Journal of Experimental Psychology: Learning, Memory, and Cognition, 15(6), 1147–1156. https://doi.org/10.1037/0278-7393.15.6.1147
Chi, M. T. H., Feltovich, P. J., & Glaser, R. (1981). Categorization and representation of physics problems by experts and novices. Cognitive Science, 5(2), 121–152. http://www.sciencedirect.com/science/article/pii/S0364021381800298
Dahl, O.-J., Dijkstra, E. W., & Hoare, C. A. R. (1972). Structured programming. Academic Press Ltd.
Denning, P.J., Comer, D. E., Gries, D., Mulder, M. C., Tucker, A., Turner, A. J., & Young, P. R. (1989). Computing as a discipline. Computer, 22(2), 63–70. https://doi.org/10.1109/2.19833
Denning, Peter J. (1985). The Science of Computing: What is computer science? American Scientist, 73(1), 16–19. http://www.jstor.org/stable/27853057
Denning, Peter J. (2003). Great Principles of Computing. Communications of the ACM, 46(11), 15–20. https://doi.org/10.1145/948383.948400
Denning, Peter J. (2017). Computational thinking in science.
Factorovich, P., & O’Connor, F. S. (2016). Cuaderno para el docente. Actividades para aprender a programar. Fundación Sadosky. http://programar.gob.ar/descargas/manual-docente-descarga-web.pdf
Faries, J. M., & Reiser, B. J. (1988). Access and Use of Previous Solutions in a Problem Solving Situation. https://apps.dtic.mil/dtic/tr/fulltext/u2/a224717.pdf
Franklin, D., Hill, C., Dwyer, H. A., Hansen, A. K., Iveland, A., & Harlow, D. B. (2016). Initialization in Scratch. Proceedings of the 47th ACM Technical Symposium on Computing Science Education - SIGCSE ’16, 217–222. https://doi.org/10.1145/2839509.2844569
Gentner, D. (1983). Structure-mapping: A theoretical framework for analogy. Cognitive Science, 7(2), 155–170. https://doi.org/https://doi.org/10.1016/S0364-0213(83)80009-3
Gentner, D. (1989). The mechanisms of analogical transfer. En S. Vosniadou & A. Ortony (Eds.), Similarity and Analogical Reasoning (pp. 199–242). Cambridge University Press.
Gentner, D., Loewenstein, J., & Thompson, L. (2003). Learning and transfer: A general role for analogical encoding. Journal of Educational Psychology, 95(2), 393–408. https://doi.org/10.1037/0022-0663.95.2.393
Gentner, D., & Markman, A. B. (1997). Structure mapping in analogy and similarity. American Psychologist, 52(1), 45–56. https://doi.org/10.1037/0003-066X.52.1.45
Gentner, D., Rattermann, M. J., & Forbus, K. D. (1993). The Roles of Similarity in Transfer: Separating Retrievability From Inferential Soundness. Cognitive Psychology, 25(4), 524–575. https://doi.org/10.1006/cogp.1993
Gick, M. L., & Holyoak, K. J. (1980). Analogical problem solving. Cognitive Psychology, 12(3), 306–355. https://doi.org/10.1016/0010-0285(80)90013-4
Gick, M. L., & Holyoak, K. J. (1983). Schema induction and analogical transfer. Cognitive Psychology, 15(1), 1–38. https://doi.org/10.1016/0010-0285(83)90002-6
Goldstone, R. L., & Son, J. Y. (2005). The Transfer of Scientific Principles Using Concrete and Idealized Simulations. Journal of the Learning Sciences, 14(1), 69–110. https://doi.org/10.1207/s15327809jls1401_4
Harel, I., & Papert, S. (1991). Constructionism. Ablex Publishing.
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Hazzan, O. (2003). How Students Attempt to Reduce Abstraction in the Learning of Mathematics and in the Learning of Computer Science. Computer Science Education, 13(2), 95–122. https://doi.org/10.1076/csed.13.2.95.14202
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Hazzan, O., & Kramer, J. (2007). Abstraction in Computer Science & Software Engineering: A Pedagogical Perspective. Frontier Journal, 4(1), 6–14.
Hazzan, O., & Kramer, J. (2016). Assessing abstraction skills. Communications of the ACM, 59(12), 43–45. https://doi.org/10.1145/2926712
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Holyoak, K. J., & Thagard, P. (1989). Analogical Mapping by Constraint Satisfaction. Cognitive Science, 13(3), 295–355. https://doi.org/10.1207/s15516709cog1303_1
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Kurtz, K. J., & Loewenstein, J. (2007). Converging on a new role for analogy in problem solving and retrieval: when two problems are better than one. Memory & Cognition, 35(2), 334–341. https://doi.org/10.3758/BF03193454
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Meerbaum-Salant, O., Armoni, M., & Ben-Ari, M. (Moti). (2010). Learning computer science concepts with Scratch. Proceedings of the Sixth international workshop on Computing education research - ICER ’10, 69–76. https://doi.org/10.1145/1839594.1839607
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spelling D’Angelo, Verónica48f53a2f-734c-4f16-9be6-ac3abf9ad44c2024-09-09T21:14:39Z2024-09-09T21:14:39Z2020-10-20ISSN: 1657-2831e-ISSN: 2539-2115http://hdl.handle.net/20.500.12749/26437instname:Universidad Autónoma de Bucaramanga UNABrepourl:https://repository.unab.edu.cohttps://doi.org/10.29375/25392115.4035En este artículo de revisión se explora una posible contribución de las investigaciones sobre razonamiento analógico al problema de la transferencia en programación -en la transición entre el aprendizaje de conceptos en la escuela media y su aplicación en la universidad. La facilidad con que los alumnos construyen programas en entornos multimedia conlleva la desventaja de una dificultad para trasladar esos conceptos a los lenguajes “reales” basados en texto, probablemente porque no se ha trabajado suficiente en promover abstracciones en el nivel del problema. Según investigaciones en enseñanza de la programación, los alumnos suelen tener mayor dificultad en los niveles de abstracción superior (la comprensión del problema) que en los niveles inferiores (como la codificación). La comparación de problemas mediante razonamiento analógico es una estrategia proveniente de la psicología cognitiva extendida a diversas disciplinas. Sugerimos que su aplicación en el campo de la enseñanza de la programación podría contribuir a solucionar el problema de la dificultad de abstracción en el nivel del problema, y facilitar la transferencia.This review article explores a possible contribution of research on analogical reasoning to the problem of transfer in programming -in the transition between the learning of concepts in middle school and their application at university. The ease with which students construct programs in multimedia environments carries the disadvantage of translating these concepts into “real” text-based languages, probably because not enough work has been done on the problem level. According to research in teaching programming, students tend to have greater difficulty at higher levels of abstraction (understanding the problem) than at lower levels (such as coding). Comparing problems through analogical reasoning is a strategy from cognitive psychology extended to various disciplines. We suggest that its application in programming teaching could contribute to solving the problem of the difficulty of abstraction at the problem level and facilitate the transfer.application/pdfspaUniversidad Autónoma de Bucaramanga UNABhttps://revistas.unab.edu.co/index.php/rcc/article/view/4035/3344https://revistas.unab.edu.co/index.php/rcc/issue/view/267Armoni, M. (2013). On Teaching Abstraction in CS to Novices. Journal of Computers in Mathematics and Science Teaching, 32(3), 265–284. https://www.learntechlib.org/p/41271Armoni, M., & Ben-Ari, M. (2013). Computer Science Concepts in Scratch. Department of Science Teaching , Weizmann Institute of Science. https://stwww1.weizmann.ac.il/scratch/scratch_en/Armoni, M., Meerbaum-Salant, O., & Ben-Ari, M. (2015). From Scratch to “Real” Programming. ACM Transactions on Computing Education, 14(4), 1–15. https://doi.org/10.1145/2677087Barnett, S. M., & Ceci, S. J. (2002). When and where do we apply what we learn?: A taxonomy for far transfer. Psychological Bulletin, 128(4), 612–637. https://doi.org/10.1037/0033-2909.128.4.612Bell, T., Rosamond, F., & Casey, N. (2012). Computer Science Unplugged and Related Projects in Math and Computer Science Popularization. En H. L. Bodlaender, R. Downey, F. V. Fomin, & D. Marx (Eds.), The MultivariateAlgorithmic Revolution and Beyond. Lecture Notes in Computer Science, vol 7370 (pp. 398–456). Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-30891-8_18Burgoon, E. M., Henderson, M. D., & Markman, A. B. (2013). There Are Many Ways to See the Forest for the Trees: A Tour Guide for Abstraction. Perspectives on Psychological Science, 8(5), 501–520. https://doi.org/10.1177/1745691613497964Çakiroğlu, Ü., Sude, S., B., K., Sari, A., Yildiz, S., & Öztürk, M. (2018). Exploring perceived cognitive load in learning programming via Scratch. Research in Learning Technology, 26. https://doi.org/10.25304/rlt.v26.1888Catrambone, R., & Holyoak, K. J. (1989). Overcoming contextual limitations on problem-solving transfer. Journal of Experimental Psychology: Learning, Memory, and Cognition, 15(6), 1147–1156. https://doi.org/10.1037/0278-7393.15.6.1147Chi, M. T. H., Feltovich, P. J., & Glaser, R. (1981). Categorization and representation of physics problems by experts and novices. Cognitive Science, 5(2), 121–152. http://www.sciencedirect.com/science/article/pii/S0364021381800298Dahl, O.-J., Dijkstra, E. W., & Hoare, C. A. R. (1972). Structured programming. Academic Press Ltd.Denning, P.J., Comer, D. E., Gries, D., Mulder, M. C., Tucker, A., Turner, A. J., & Young, P. R. (1989). Computing as a discipline. Computer, 22(2), 63–70. https://doi.org/10.1109/2.19833Denning, Peter J. (1985). The Science of Computing: What is computer science? American Scientist, 73(1), 16–19. http://www.jstor.org/stable/27853057Denning, Peter J. (2003). Great Principles of Computing. Communications of the ACM, 46(11), 15–20. https://doi.org/10.1145/948383.948400Denning, Peter J. (2017). Computational thinking in science.Factorovich, P., & O’Connor, F. S. (2016). Cuaderno para el docente. Actividades para aprender a programar. Fundación Sadosky. http://programar.gob.ar/descargas/manual-docente-descarga-web.pdfFaries, J. M., & Reiser, B. J. (1988). Access and Use of Previous Solutions in a Problem Solving Situation. https://apps.dtic.mil/dtic/tr/fulltext/u2/a224717.pdfFranklin, D., Hill, C., Dwyer, H. A., Hansen, A. K., Iveland, A., & Harlow, D. B. (2016). Initialization in Scratch. Proceedings of the 47th ACM Technical Symposium on Computing Science Education - SIGCSE ’16, 217–222. https://doi.org/10.1145/2839509.2844569Gentner, D. (1983). Structure-mapping: A theoretical framework for analogy. Cognitive Science, 7(2), 155–170. https://doi.org/https://doi.org/10.1016/S0364-0213(83)80009-3Gentner, D. (1989). The mechanisms of analogical transfer. En S. Vosniadou & A. Ortony (Eds.), Similarity and Analogical Reasoning (pp. 199–242). Cambridge University Press.Gentner, D., Loewenstein, J., & Thompson, L. (2003). Learning and transfer: A general role for analogical encoding. Journal of Educational Psychology, 95(2), 393–408. https://doi.org/10.1037/0022-0663.95.2.393Gentner, D., & Markman, A. B. (1997). 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Pearson Education.Vol. 21 Núm. 2 (2020): Revista Colombiana de Computación (Julio-Diciembre); 71-82Programación de ordenadoresRazonamiento analógicoAbstracciónTransferenciaComputer programmingAnalogical reasoningAbstractionTransferPosibles aportes del razonamiento analógico al problema de la abstracción y transferencia en la enseñanza de programaciónPossible contributions of analogical reasoning to the problem of abstraction and transfer in programming teachinginfo:eu-repo/semantics/articleArtículohttp://purl.org/coar/resource_type/c_2df8fbb1http://purl.org/redcol/resource_type/ARThttp://purl.org/coar/version/c_970fb48d4fbd8a85http://purl.org/coar/access_right/c_abf2ORIGINALArtículo.pdfArtículo.pdfArtículoapplication/pdf455914https://repository.unab.edu.co/bitstream/20.500.12749/26437/1/Art%c3%adculo.pdf2eee5b068394f579a5096162b6478f91MD51open accessLICENSElicense.txtlicense.txttext/plain; charset=utf-8347https://repository.unab.edu.co/bitstream/20.500.12749/26437/2/license.txt855f7d18ea80f5df821f7004dff2f316MD52open accessTHUMBNAILArtículo.pdf.jpgArtículo.pdf.jpgIM Thumbnailimage/jpeg10000https://repository.unab.edu.co/bitstream/20.500.12749/26437/3/Art%c3%adculo.pdf.jpg88323eb32fbd888896dbdc4189af2c55MD53open access20.500.12749/26437oai:repository.unab.edu.co:20.500.12749/264372024-09-09 22:01:15.282open accessRepositorio Institucional | Universidad Autónoma de Bucaramanga - UNABrepositorio@unab.edu.coTGEgUmV2aXN0YSBDb2xvbWJpYW5hIGRlIENvbXB1dGFjacOzbiBlcyBmaW5hbmNpYWRhIHBvciBsYSBVbml2ZXJzaWRhZCBBdXTDs25vbWEgZGUgQnVjYXJhbWFuZ2EuIEVzdGEgUmV2aXN0YSBubyBjb2JyYSB0YXNhIGRlIHN1bWlzacOzbiB5IHB1YmxpY2FjacOzbiBkZSBhcnTDrWN1bG9zLiBQcm92ZWUgYWNjZXNvIGxpYnJlIGlubWVkaWF0byBhIHN1IGNvbnRlbmlkbyBiYWpvIGVsIHByaW5jaXBpbyBkZSBxdWUgaGFjZXIgZGlzcG9uaWJsZSBncmF0dWl0YW1lbnRlIGludmVzdGlnYWNpw7NuIGFsIHDDumJsaWNvIGFwb3lhIGEgdW4gbWF5b3IgaW50ZXJjYW1iaW8gZGUgY29ub2NpbWllbnRvIGdsb2JhbC4=

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