Learning with computer simulations: a case study on reservoir temperatures in carnot cycles

Autores/as

DOI:

https://doi.org/10.22600/1518-8795.ienci2024v29n3p172

Palabras clave:

Simulación por computadora, calor y temperatura, aprendizaje conceptual, ciclos de Carnot

Resumen

Las simulaciones por computadora han desempeñado un papel significativo en el desarrollo de la física, así como también en la educación en física. Los investigadores han abordado si las simulaciones promueven el aprendizaje, pero pocos estudios han investigado cómo las simulaciones participan realmente en los procesos de aprendizaje. Este estudio busca describir cómo las simulaciones participan en el aprendizaje conceptual. Se lleva a cabo un estudio de caso utilizando entrevistas grabadas en video con tres grupos de estudiantes universitarios mientras abordan una tarea de resolución de problemas sobre termodinámica (ciclos de Carnot). Los estudiantes utilizan una simulación desarrollada específicamente para apoyo. El análisis se basa en la Teoría de la Clase de Coordinación (CCT). Los resultados indican que los estudiantes no solo utilizan la simulación para pensar; en realidad, es parte de lo que piensan. Se descubrió que los estudiantes participan en tres dinámicas de interacción diferentes con la simulación. Sintonizados con la CCT, estos fueron codificados como interacciones Extractivas/Inferenciales/Articulativas. En cada caso, se describe la sustancia de cómo estas interacciones contribuyen al aprendizaje conceptual. Se proporcionan implicaciones para futuras investigaciones y para la enseñanza.

Referencias

Adams, W. K., Reid, S., LeMaster, R., McKagan, S., Perkins, K., Dubson, M., & Wieman, C. E. (2008). A study of educational simulations Part II-Interface Design. Journal of Interactive Learning Research, 19(4), 551-577. Recovered from: https://www.learntechlib.org/p/24364/

Alessi, S. M., & Trollip, S. R. (1984). Computer-based instruction: Methods and development. Prentice-Hall, Inc.

Amin, T. G., & Levrini, O. (2017). Facing the challenge of programmatic research on conceptual change. In Tamer & Levrini (Eds.) Converging Perspectives on Conceptual Change (pp. 334-351). London and New York: Routledge

Amin, T. G., Smith, C. L., & Wiser, M. (2014). Student conceptions and conceptual change: Three overlapping phases of research. In N. G. Lederman & S. K. Abell (Eds.) Handbook of research on science education (pp. 600-620). New York, United States of America: Routledge.

Barth‐Cohen, L. A., & Wittmann, M.C. (2017). Aligning coordination class theory with a new context: Applying a theory of individual learning to group learning. Science Education, 101(2), 333–363. https://doi.org/10.1002/sce.21264

Baser, M. (2006). Effects of conceptual change and traditional confirmatory simulations on pre-service teachers' understanding of direct current circuits. Journal of Science Education and Technology, 15, 367-381. https://doi.org/10.1007/s10956-006-9025-3

Basu, S., Sengupta, P., & Biswas, G. (2015) A Scaffolding Framework to Support Learning of Emergent Phenomena Using Multi-Agent-Based Simulation Environments. Research in Science Education, 45, 293-324. https://doi.org/10.1007/s11165-014-9424-z

Bell, R. L., & Trundle, K. C. (2008). The use of a computer simulation to promote scientific conceptions of moon phases. Journal of Research in Science Teaching, 45(3), 346-372.

https://doi.org/10.1002/tea.20227

Berners-Lee, T. (1999). Weaving the Web: The original design and ultimate destiny of the World Wide Web by its inventor. San Francisco, United States of America: Harper.

Bing, T. J., & Redish, E. F. (2012). Epistemic complexity and the journeyman-expert transition. Physical Review Special Topics-Physics Education Research, 8(1), 010105(11). https://doi.org/10.1103/PhysRevSTPER.8.010105

Buteler, L., & Coleoni, E. (2016) Solving problems to learn concepts, how does it happen? A case for buoyancy. Physical Review Physics Education Research, 12(2), 020144(12.

https://doi.org/10.1103/PhysRevPhysEducRes.12.020144

Colella, V. (2000) Participatory Simulations: Building Collaborative Understanding Through Immersive Dynamic Modeling. Journal of the Learning Sciences, 9(4), 471-500. http://dx.doi.org/10.1207/S15327809JLS0904_4

Develaki, M. (2019). Methodology and epistemology of computer simulations and implications for science education. Journal of Science Education and Technology, 28(4), 353-370. https://doi.org/10.1007/s10956-019-09772-0

diSessa, A., & Sherin, B. L. (1998) What changes in conceptual change? International Journal of Science Education, 20(10), 1155-1191. http://dx.doi.org/10.1080/0950069980201002

diSessa, A., & Wagner, J. F. (2005) What coordination has to say about transfer. In José P. Mestre (Ed.), Transfer of learning from a modern multi-disciplinary perspective (pp.121-154) Greenwich, United States of America: Information Age Publishing.

diSessa, A., Sherin, B., & Levin, M. (2016), Knowledge analysis: An introduction. In A. A. diSessa, M. Levin, & N. Brown (Eds.), Knowledge and interaction: A synthetic agenda for the learning sciences (30-71). New York., United States of America: Routledge. https://doi.org/10.4324/9781315757360

Dufresne, R., Mestre, J., Thaden-Koch, T., Gerace, W., & Leonard, W. (2005) Knowledge Representation and Coordination in the Transfer Process. In José P. Mestre (Ed.) Transfer of learning from a modern multi-disciplinary perspective. (155-215). Greenwich, United States of America: Information Age Publishing.

Gorsky, P., & Finegold, M. (1992). Using Computer Simulation to Restructure Students' Conceptions of Force. Journal of Computers in Mathematics and science teaching, 11(2), 163-78.

Greca, I. M., Seoane, E., & Arriassecq, I. (2014). Epistemological issues concerning computer simulations in science and their implications for science education. Science & Education, 23, 897-921. https://doi.org/10.1007/s11191-013-9673-7

Halldén, O., Haglund, L., & Strömdahl, H. (2007). Conceptions and contexts: On the interpretation of interview and observational data. Educational Psychologist, 42(1), 25-40.

https://doi.org/10.1080/00461520709336916

Hammer, D., & Berland, L. (2013) Confusing Claims for Data: A Critique of Common Practices for Presenting Qualitative Research on Learning. Journal of the Learning Sciences. 23(1), 37-46. https://doi.org/10.1080/10508406.2013.802652

Hargrave, C. P., & Kenton, J. M. (2000). Preinstructional simulations: Implications for science classroom teaching. Journal of Computers in Mathematics and Science Teaching, 19(1), 47-58. Recovered from https://www.learntechlib.org/p/8063/

Hutchins, E. (1991). The social organization of distributed cognition. In L. B. Resnick, J. M. Levine & S. D. Teasley (Eds). Perspectives on socially shared cognition (pp. 283-307). American Psychological Association. https://doi.org/10.1037/10096-012

Ingar, U., & Kraushaar, W.L. (1984) Introducción al estudio de la Mecánica, Materia y Ondas. Buenos Aires: Reverté.

Jordan, B., & Henderson, A. (1995). Interaction analysis: Foundations and practice. The Journal of the Learning Sciences. 4(1), 39-103. https://doi.org/10.1207/s15327809jls0401_2

Kluge, A. (2019) Learning science with an interactive simulator: negotiating the practice-theory barrier. International Journal of Science Education, 41(8), 1071-1095. https://doi.org/10.1080/09500693.2019.1590881

Koneman, E. W., Allen, S. D., Janda, W. M., Schreckenberger, P. C., & Winn, W. C. (2006). Diagnóstico Microbiologico Texto y Atlas a Color. (6a ed.). Buenos Aires, Argentina: Medica Panamericana.

Krajcik, J. S., & Mun, K. (2014) Promises and challenges of using learning technologies to promote student learning of science. In N. Lederman & S. Abell (Eds.) Handbook of research on science education (pp 337-360). New York, United States of America: Routledge.

Lally, D., & Forbes, C. (2019) Modelling water systems in an introductory undergraduate course: Students’ use and evaluation of data-driven, computer-based models. International Journal of Science Education, 41(14). https://doi.org/10.1080/09500693.2019.1657252

Levin, M. E. (2012) Modelling the co-development of strategic and conceptual knowledge dring mathematical problem solving. [unpublished doctoral dissertation] University of California, Berkeley.

Levrini, O., & diSessa, A. (2008) How students learn from multiple contexts and definitions: Proper time as a coordination class. Physical Review Physics Education Research. 4(1)

https://doi.org/10.1103/PhysRevSTPER.4.010107

Lewis, C. (2012). Applications of out-of-domain knowledge in students’ reasoning about computer program state [Unpublished doctoral dissertation]. University of California, Berkeley.

Lowe, R. (2004). Interrogation of a dynamic visualization during learning. Learning and Instruction, 14(3), 257-274. https://doi.org/10.1016/j.learninstruc.2004.06.003

Martinez, G., Naranjo, F. L., Perez, A. L., Suero, M. I., & Pardo, P. J. (2011). Comparative study of the effectiveness of three learning environments: Hyper-realistic virtual simulations, traditional schematic simulations and traditional laboratory. Physical Review Special Topics-Physics Education Research, 7(2). https://doi.org/10.1103/PhysRevSTPER.7.020111

Parnafes, O., & diSessa, A. (2013). Microgenetic learning analysis: A methodology for studying knowledge in transition. Human Development, 56(1), 5-37. https://doi.org/10.1159/000342945

Parnafes, O. (2007) What Does “Fast” Mean? Understanding the Physical World Through Computational Representations. Journal of the Learning Sciences, 16(3), 415-450. https://doi.org/10.1080/10508400701413443

Posner, G. J., Strike, K. A., Hewson, P. W., & Gertzog, W. A. (1982). Toward a theory of conceptual change. Science education, 66(2), 211-227. https://doi.org/10.1002/sce.3730660207

Ramasundaram, V., Grunwald, S., Mangeot, A., Comerford, N. B., & Bliss, C. M. (2005). Development of an environmental virtual field laboratory. Computers & Education, 45(1), 21-34. https://doi.org/10.1016/j.compedu.2004.03.002

Resnick, R, Halliday, D., & Krane, K.S. (2010) Física. Vol 1 (8a. ed.). Mexico, Mexico: Grupo Editorial Patria.

Ronen, M., & Eliahu, M. (2000). Simulation—A bridge between theory and reality: The case of electric circuits. Journal of computer assisted learning, 16(1), 14-26. https://doi.org/10.1046/j.1365-2729.2000.00112.x

Rose, D. H., & Meyer, A. (2002). A Teaching Every student in the Digital Age: Universal Design for learning. Association for Supervision and Curriculum Development, 1703 N. Beauregard St., Alexandria, VA 22311-1714, 2002.

Sears, F.W., & Salinger, G.L. (1980) Termodinámica, teoría cinética y termodinámica estadística. Barcelona, España: Reverté.

Sengupta, P., Krinks, K.D., & Clark, D.B. (2015). Learning to Deflect: Conceptual Change in Physics during Digital Game Play. Journal of the Learning Sciences, 24(4), 638-674. https://doi.org/10.1080/10508406.2015.1082912

Serway, R., & Jewett Jr., J.W. (2018) Física para ciencias e ingeniería. Vol 1. (10a. ed.) Mexico, Mexico: Cengage Learner Editores.

Smetana, L. K., & Bell, R. L. (2012). Computer simulations to support science instruction and learning: A critical review of the literature. International Journal of Science Education, 34(9), 1337-1370. https://doi.org/10.1080/09500693.2011.605182

Smith, T. I., Christensen, W. M., Mountcastle, D. B., & Thompson, J. R. (2015). Identifying student difficulties with entropy, heat engines, and the Carnot cycle. Physical Review Special Topics-Physics Education Research, 11(2). https://doi.org/10.1103/PhysRevSTPER.11.020116

Trundle, K. C., & Bell, R. L. (2010). The use of a computer simulation to promote conceptual change: A quasi-experimental study. Computers & Education, 54(4), 1078-1088. https://doi.org/10.1016/j.compedu.2009.10.012

Velasco, J., & Buteler, L. (2017). Simulaciones computacionales en la enseñanza de la física: una revisión crítica de los últimos años. Enseñanza de las ciencias: revista de investigación y experiencias didácticas, 35(2), 161-178. https://doi.org/10.5565/rev/ensciencias.2117

Velasco, J., Buteler, L., & Coleoni, E. (2021). Conceptual development through computer simulations: a case study in physics. Revista de Enseñanza de la Física, 33(2), 529-536. https://doi.org/10.5565/rev/ensciencias.2117

Velasco, J., Buteler, L., Briozzo, C., & Coleoni, E. (2022) Learning Entropy Among Peers Through the Lens of Coordination Class Theory. Physical Review Physics Education Research, 18. https://doi.org/10.1103/PhysRevPhysEducRes.18.010127

Villarreal, M.E., & Borba, M.C. (2010) Collectives of humans-with-media in mathematics education: notebooks, blackboards, calculators, computers and … notebooks throughout 100 years of ICMI. ZDM Mathematics Education, 42, 49–62. https://doi.org/10.1007/s11858-009-0207-3

Vlachopoulos, D., & Makri, A. (2017). The effect of games and simulations on higher education: a systematic literature review. International Journal of Educational Technology in Higher Education, 14(1), 1-33. https://doi.org/10.1186/s41239-017-0062-1

Vosniadou, S. (Ed.). (2008). International handbook of research on conceptual change. New York, United States of America: Routledge

Windschitl, M. (2001). Using simulations in the middle school: Does assertiveness of dyad partners influence conceptual change? International Journal of Science Education, 23(1), 17-32. https://doi.org/10.1080/09500690121082

Wittmann, M. C. (2002) The Object Coordination Class Applied to Wave Pulses: Analysing Student Reasoning in Wave Physics. International Journal of Science Education, 24(1), 97-118. https://doi.org/10.1080/09500690110066944

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Publicado

2024-12-26

Cómo citar

Velasco, J. J., Buteler, L. M., & Coleoni, E. A. (2024). Learning with computer simulations: a case study on reservoir temperatures in carnot cycles. Investigaciones En Enseñanza De Las Ciencias, 29(3), 172-190. https://doi.org/10.22600/1518-8795.ienci2024v29n3p172