Abstract
Simulation and Computation make a versatile teaching strategy, and may be an important way to motivate students and lecturers to achieve meaningful learning. Indeed, this work refers to a study whose main objective is to set the influence that a teaching approach based on the use of computer simulation would have on students’ learning, compared to the one in use today. This work involved the participation of two classes of 11th grade at a Secondary School in Lisbon, Portugal, where the main goal is to teach a specific topic to an untried student’s group. With regard to the simulation environment, it will be grounded on a Proof Theoretical approach to Knowledge Representation and Reasoning, which caters for the handling of incomplete, unknown or even self-contradictory information or knowledge.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Chapman, N., Chapman, J.: Digital Multimedia. Wiley, Chichester (2009)
Fluckiger, F.: Understanding Networked Multimedia: Applications and Technology. Prentice-Hall, Upper Saddle River (1995)
Sweller, J.: Evolution of human cognitive architecture. In: Ross, B. (ed.) The Psychology of Learning and Motivation, vol. 43, pp. 215–266. Academic Press, San Diego (2003)
Clark, R.C., Nguyen, F., Sweller, J.: Efficiency in Learning: Evidence-Based Guidelines to Manage Cognitive Load. Pfeiffer, San Francisco (2006)
Schnotz, W., Bannert, M.: Construction and interference in learning from multiple representation. Learn. Instr. 13, 141–156 (2003)
Mayer, R.E.: Multimedia learning: are we asking the right questions? Educ. Psychol. 32, 1–19 (1997)
Mayer, R.E.: Multimedia Learning. Cambridge University Press, Cambridge (2009)
de Jong, T., van Joolingen, W.R.: Scientific discovery learning with computer simulations of conceptual domains. Rev. Educ. Res. 68, 179–201 (1998)
Mayer, R.E.: Should there be a three strikes rule against pure discovery learning? Am. Psychol. 59, 14–19 (2004)
Jimoyiannis, A., Komis, V.: Computer simulations in physics teaching and learning: a case study on students’ understanding of trajectory motion. Comput. Educ. 36, 183–204 (2001)
Scalise, K., Timms, M., Moorjani, A., Clark, L., Holtermann, K., Irvin, P.: Student learning in science simulations: design features that promote learning gains. J. Res. Sci. Teach. 48, 1050–1078 (2011)
Rutten, N., van Joolingen, W.R., van der Veen, J.: The learning effects of computer simulations in science education. Comput. Educ. 58, 136–253 (2012)
Franco-Mariscal, A.J., Oliva-Martínez, J.M., Blanco-López, Á., España-Ramos, E.: A game-based approach to learning the idea of chemical elements and their periodic classification. J. Chem. Educ. (2016). doi:10.1021/acs.jchemed.5b00846
Kakas, A., Kowalski, R., Toni, F.: The role of abduction in logic programming. In: Gabbay, D., Hogger, C., Robinson, I. (eds.) Handbook of Logic in Artificial Intelligence and Logic Programming, vol. 5, pp. 235–324. Oxford University Press, Oxford (1998)
Pereira, L., Anh, H.: Evolution prospection. In: Nakamatsu, K. (ed.) New Advances in Intelligent Decision Technologies – Results of the First KES International Symposium IDT 2009, Studies in Computational Intelligence, vol. 199, pp. 51–64. Springer, Heidelberg (2009)
Neves, J.: A logic interpreter to handle time and negation in logic databases. In: Muller, R., Pottmyer, J. (eds.) Proceedings of the 1984 Annual Conference of the ACM on the 5th Generation Challenge, pp. 50–54. Association for Computing Machinery, New York (1984)
Neves, J., Machado, J., Analide, C., Abelha, A., Brito, L.: The halt condition in genetic programming. In: Neves, J., Santos, M.F., Machado, J. (eds.) Progress in Artificial Intelligence. LNAI, vol. 4874, pp. 160–169. Springer, Heidelberg (2007)
University of Colorado: Interactive Simulations for Science and Math. https://phet.colorado.edu/en/simulation/reactants-products-and-leftovers
Rao A.S.: AgentSpeak(L): BDI agents speak out in a logical computable language. In: Van de Velde, W., Perram, J.W. (eds.) Agents Breaking Away. LNCS, vol. 1038, pp. 42–55. Springer, Heidelberg (1996)
Acknowledgments
This work has been supported by COMPETE: POCI-01-0145-FEDER-007043 and FCT – Fundação para a Ciência e Tecnologia within the Project Scope: UID/CEC/00319/2013.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Figueiredo, M., Rafael, C., Neves, J., Vicente, H. (2017). Assessing the Role of Computer Simulation in Chemistry Learning. In: Vittorini, P., et al. Methodologies and Intelligent Systems for Technology Enhanced Learning. MIS4TEL 2017. Advances in Intelligent Systems and Computing, vol 617. Springer, Cham. https://doi.org/10.1007/978-3-319-60819-8_6
Download citation
DOI: https://doi.org/10.1007/978-3-319-60819-8_6
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-60818-1
Online ISBN: 978-3-319-60819-8
eBook Packages: EngineeringEngineering (R0)