• Sara Moutinho
  • Rui Moura
  • Clara VasconcelosEmail author


Nowadays, meaningful learning takes a central role in science education and is based in mental models that allow the representation of the real world by individuals. Thus, it is essential to analyse the student’s mental models by promoting an easier reconstruction of scientific knowledge, by allowing them to become consistent with the curricular models presented in the classroom. In this context, the study aims to examine, through the application of a diagnostic instrument (Two-Tier Diagnostic Test), what students consider to be the seismic effects on soils and buildings, to analyse and to compare their mental models about some of these issues related to seismology, applying a questionnaire to 52 students from a Portuguese University attending an undergraduate degree in Geology and a master course in Biology and Geology teaching. The analysis of the data allowed concluding that undergraduate students have more inconsistent mental models than master students, mainly concerning the factors which influence the seismic risk, such as hazard and vulnerability, and the soils characteristics which influence the intensity of earthquakes. During their academic formation in the university, teachers present some curricular models to students which allow them to reconstruct their mental models and turn them scientifically consistent, enhancing the educational implications of this study that points to the need for teachers to be aware of the importance of the diagnosis of the students’ mental models and to promote meaningful learning and scientific literacy autonomously and dynamically.


curricular models meaningful learning mental models scientific literacy seismology 


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  1. A-Balushi, S. M. (2011). Students’ evaluation of the credibility of scientific models that represent natural entities and phenomena. International Journal of Science and Mathematics Education, 9(3), 571–601.CrossRefGoogle Scholar
  2. Atkinson, G. M. (2004). An overview of development in seismic hazard analysis. 13th World Conference on Earthquake Engineering (paper no. 5001). Vancouver, Canada.Google Scholar
  3. Bell, F. G. (2003). Earthquake activity. Geological hazards: Their assessment, avoidance and mitigation, pp.62– 93. London: Spon Press.Google Scholar
  4. Cernuzzi, L. & Zambonelli, F. (2008). Profile based comparative analysis for AOSE methodologies evaluation. In R. L. Wainwright & H. M. Haddad (Eds.), Proceedings of the 2008 ACM symposium on applied computing (pp. 60–65). New York, NY: ACM.CrossRefGoogle Scholar
  5. Clement, J. (2000). Model based learning as a key research area for science education. International Journal of Science Education, 22(9), 1041–1053.CrossRefGoogle Scholar
  6. Clement, J. (2008). Student/teacher co-construction of visualizable models in large group discussion. In J. J. Clement & M. A. Rea-Ramirez (Eds.), Model based learning and instruction in science (pp. 11–22). New York, NY: Springer.CrossRefGoogle Scholar
  7. Coll, R. K., France, B. & Taylor, I. (2005). The role of models and analogies in science education: Implications from research. International Journal of Science Education, 27(2), 183–198.CrossRefGoogle Scholar
  8. Francek, M. (2013). A compilation and review of over 500 Geoscience misconceptions. International Journal of Science Education, 35(1), 31–64.CrossRefGoogle Scholar
  9. Glade, T., Anderson, M. & Crozier, M. (2005). Landslide hazard and risk, 41–218. Chichester, England: Wiley.CrossRefGoogle Scholar
  10. Gobert, J. D. & Buckley, B. C. (2000). Introduction to model-based teaching and learning in science education. International Journal of Science Education, 22(9), 891–894.CrossRefGoogle Scholar
  11. Greca, I. M. & Moreira, M. A. (1997). The kinds of mental representations—Models, propositions and images—Used by college physics students regarding the concept of field. International Journal of Science Education, 19(6), 711–724.CrossRefGoogle Scholar
  12. Greca, I. M. & Moreira, M. A. (2000). Mental models, conceptual models, and modeling. International Journal of Science Education, 22(1), 1–11.CrossRefGoogle Scholar
  13. Grotzinger, J. & Jordan, T. (2010). Earthquakes. Understanding Earth, pp.337–367. New York: W.H. Freeman and Company.Google Scholar
  14. Johnson-Laird, P. N. (1983). Inference and mental models. Mental models. Towards a cognitive science of language, inference and consciousness, pp.126–146. Cambridge: Harvard University Press.Google Scholar
  15. Johnson-Laird, P. N. (1996). Images, models, and propositional representations. In M. De Vega, M. J. Intons-Peterson, P. N. Johnson-Laird, M. Denis & M. Marschark (Eds.), Models of visuospatial cognition (pp. 90–127). New York, NY: Oxford University Press.Google Scholar
  16. Johnson-Laird, P. N. (2001). Mental models and deduction. Trends in Cognitive Sciences, 5(10), 434–442.CrossRefGoogle Scholar
  17. Justi, R. (2006). La enseñanza de ciencias basada en la elaboración de modelos. Enseñanza de las Ciencias, 24(2), 173–184.Google Scholar
  18. Justi, R. (2009). Learning how to model in science classroom: Key teachers’ role in supporting the development of students’ modelling skills. Educación Química, 20, 32–40.Google Scholar
  19. Justi, R. S. & Gilbert, J. K. (2002). Science teachers’ knowledge about and attitudes towards the use of models and modelling in learning science. International Journal of Science Education, 24(12), 1273–1292.CrossRefGoogle Scholar
  20. Kirkby, K. (2011). ‘Easier to address’ earth science misconceptions. Retrieved June 28, 2013, from
  21. Krapas, S., Queiroz, G., Colinvaux, D. & Franco, C. (1997). Modelos: Uma análise de sentidos na literatura de pesquisa em ensino de ciências. Investigações em Ensino de Ciências, 2(3), 185–205.Google Scholar
  22. Lin, J. W. (2013). Elementary school teachers’ knowledge of model functions and modelling processes: A comparison of science and non-science majors. International Journal of Science and Mathematics Education. doi: 10.1007/s10763-013-9446-4.Google Scholar
  23. Liu, S. C. (2005). Models of “The Heavens and the Earth”: An investigation of German and Taiwanese students’ alternative conceptions of the Universe. International Journal of Science and Mathematics Education, 3, 295–325.CrossRefGoogle Scholar
  24. Massa, M., Marzorati, S., Ladina, C. & Lovati, S. (2010). Urban seismic stations: Soil–structure interaction assessment by spectral ratio analyses. Bulletin of Earthquake Engineering, 8, 723–738.CrossRefGoogle Scholar
  25. Mikkilä-Erdmann, M., Penttinen, M., Anto, E. & Olkinuora, E. (2008). Constructing mental models during learning from science text: Eye tracking methodology meets conceptual change. In D. Ifenthaler, P. Pirnay-Dummer & J. M. Spector (Eds.), Understanding models for learning and instruction (pp. 63–80). New York, NY: Springer.Google Scholar
  26. Monteiro, A., Nóbrega, C., Abrantes, I. & Gomes, C. (2012). Diagnosing Portuguese students’ misconceptions about the mineral concept. International Journal of Science Education, 34(17), 2705–2726.CrossRefGoogle Scholar
  27. Moreira, M. A., Greca, I. M. & Rodriguez, P. M. L. (2002). Modelos mentales y modelos conceptuales en la enseñanza/aprendizaje de las Ciencias. Revista Brasileira de Pesquisa em Educação em Ciências, 2(3), 37–57.Google Scholar
  28. Moura, R., Sousa, M., Fernandes, I., Cunha, M., Afonso, D., Paulo, J., …, Borges, L. (2009). GEORISK: A geologic risk map for the World Heritage Area of Porto city (North Portugal). In R. Moura (Chair), Cartography and GIS. Symposium conducted at the meeting of 9th International Multidisciplinary Scientific GeoConference, Bulgaria.Google Scholar
  29. Moura, R., Umaraliev, R., Dal Moro, G. & Noronha, F. (2012). Preliminary results of dispersive wave vs measurements in the granitic urban environment of Porto, Portugal. 12th International Multidisciplinary Scientific GeoConference—SGEM2012, Conference Proceedings, ISBN 1314–2704, Bulgaria (pp. 625–634).Google Scholar
  30. Moutinho, S., Torres, J., Almeida, A. & Vasconcelos, C. (2013). Portuguese teachers’ views about geosciences models. Enseñanza de las Ciencias, Vol. Extra: “La investigación en didáctica de las ciencias. Un compromiso con la sociedad del conocimiento”, IX Congreso Internacional sobre Investigación en Didáctica de las Ciencias, 2430–2435.Google Scholar
  31. Norman, D. A. (1983). Some observations on mental models. In D. Gentner & A. L. Stevens (Eds.), Mental models (pp. 7–14). New Jersey, NJ: Lawrence Erlbaum Associates.Google Scholar
  32. Palmero, M. L. R. (2008). La Teoría de los Modelos Mentales de Johnson-Laird. In M. L. R. Palmero et al. (Eds.), La Teoría del Aprendizaje Significativo en la perspectiva de la Psicología Cognitiva (pp. 46–87). Barcelona, Spain: Editorial Octaedro.Google Scholar
  33. Rook, L. (2013). Mental models: A robust definition. The Learning Organization, 20(1), 38–47.CrossRefGoogle Scholar
  34. Serafini, O. (1981). Indicadores Cuantitativos de la Distancia Evaluativa: Coeficientes y Congruencia Simple (C) y Ponderada (Cp). Brazil: Brasilia.Google Scholar
  35. Serafini, O. (1988). Análisis de Perfiles en Ciencias de la Educación: Coeficientes ES1 y ES2 de Similaridad Configuracional entre Perfiles Cuantitativos. Revista Paraguaya de Sociología, 72, 193–200.Google Scholar
  36. Torres, J., Moutinho, S., Almeida, A. & Vasconcelos, C. (2013). Portuguese science teachers’ views about Nature of Science and Scientific Models. Enseñanza de las Ciencias, Vol. Extra: “La investigación en didáctica de las ciencias. Un compromiso con la sociedad del conocimiento”, IX Congreso Internacional sobre Investigación en Didáctica de las Ciencias), 3541–3546.Google Scholar
  37. Treagust, D. (1986). Evaluating students’ misconceptions by means of diagnostic multiple choice items. Research In Science Education, 16, 199–207.CrossRefGoogle Scholar
  38. Treagust, D. F. (1988). Development and use of diagnostic tests to evaluate students’ misconceptions in science. International Journal of Science Education, 10(2), 159–169.CrossRefGoogle Scholar

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© Springer Science + Business Media B.V. 2014

Authors and Affiliations

  1. 1.Geology Centre, Faculty of SciencesUniversity of PortoPortoPortugal
  2. 2.Geology Centre, Faculty of Sciences, Department of Geoscience, Environment and Spatial Planning, Unit of Science TeachingUniversity of PortoPortoPortugal
  3. 3.Geology Centre, Faculty of Sciences, Department of Geoscience, Environment and Spatial PlanningUniversity of PortoPortoPortugal

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