Journal of Science Education and Technology

, Volume 27, Issue 4, pp 322–333 | Cite as

Integrating Computational Science Tools into a Thermodynamics Course

  • Camilo Vieira
  • Alejandra J. Magana
  • R. Edwin García
  • Aniruddha Jana
  • Matthew Krafcik


Computational tools and methods have permeated multiple science and engineering disciplines, because they enable scientists and engineers to process large amounts of data, represent abstract phenomena, and to model and simulate complex concepts. In order to prepare future engineers with the ability to use computational tools in the context of their disciplines, some universities have started to integrate these tools within core courses. This paper evaluates the effect of introducing three computational modules within a thermodynamics course on student disciplinary learning and self-beliefs about computation. The results suggest that using worked examples paired to computer simulations to implement these modules have a positive effect on (1) student disciplinary learning, (2) student perceived ability to do scientific computing, and (3) student perceived ability to do computer programming. These effects were identified regardless of the students’ prior experiences with computer programming.


Computational science Simulations Thermodynamics Self-beliefs Conceptual change Computational thinking 



The research reported in this paper was supported in part by the US National Science Foundation under the awards no. EEC1449238 and no. EEC1329262. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Science Foundation.

Compliance with Ethical Standards

All procedures in this study that involved human participants were in accordance with the ethical standards of the institution and were approved by the Institutional Review Board (IRB) before the implementation of the research procedures. All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards. For this type of study, formal consent is not required.


  1. Alabi, O., Magana, A.J. and Garcia, R.E. (2015). Gibbs, computational simulation as a teaching tool for students’ understanding of thermodynamics of materials concepts. Journal of Materials Education, 37(5-6), 239-260. Google Scholar
  2. Anderson, C., & Smith, E. (1987). Teaching science. In V. Richardson-Koehler (Ed.), The educator’s handbook: a research perspective (pp. 84–111). New York: Longman.Google Scholar
  3. Baher, J. (1999). Articulate virtual labs in thermodynamics education: a multiple case study. J Eng Educ, 88(4), 429–434. Scholar
  4. Bartol, A., Cool, T., & García, R. E. (2014). The virtual kinetics of materials laboratory. Retrieved from
  5. Chi, M. T. H., Roscoe, R. D., Slotta, J. D., Roy, M., & Chase, C. C. (2012). Misconceived causal explanations for emergent processes. Cogn Sci, 36(1), 1–61. Scholar
  6. Cobourn, W. G., & Lindauer, G. C. (1994). A flexible multimedia instructional module for introductory thermodynamics. J Eng Educ, 83(3), 271–277. Scholar
  7. Cohen, J. (1988). Statistical power analysis for the behavioral sciences (pp. 20–26). Hillsdale: Lawrence Earlbaum Associates.Google Scholar
  8. Cool, T., Bartol, A., Kasenga, M., Modi, K., & García, R. E. (2010). CALPHAD: computer coupling of phase diagrams and thermochemistry Gibbs: phase equilibria and symbolic computation of thermodynamic properties. CALPHAD: Computer Coupling of Phase Diagrams and Thermochemistry, 34(4), 393–404. Scholar
  9. Cronbach, L. J. (1951). Coefficient alpha and the internal structure of tests. Psychometrika, 16(3), 297–334. Scholar
  10. DeHoff, R. (2006). Thermodynamics in materials science (2nd ed.). Boca Raton; Taylor & Francis.Google Scholar
  11. Duit, R. (1999). Conceptual change approaches in science education. In W. Schnotz, S. Vosniadou & M. Carretero (Eds.), New perspectives on conceptual change (pp. 263–282). Oxford: Elsevier ScienceGoogle Scholar
  12. Duit, R., & Treagust, D. F. (2003). Conceptual change: a powerful framework for improving science teaching and learning. Int J Sci Educ, 25(6), 671–688. Scholar
  13. Eisinga, R., Grotenhuis, M.. & Pelzer, B. (2013) Int J Public Health 58(4), 637–642.
  14. Khan, S. (2011). New pedagogies on teaching science with computer simulations. J Sci Educ Technol, 20(3), 215–232. Scholar
  15. Kline, P. (1999). The handbook of psychological testing (2nd ed.). London: Routledge.Google Scholar
  16. Magana, A. J., & Mathur, J. (2012). Motivation, Awareness and Perceptions of Computational Science. Computing in Science and Engineering (CiSE). IEEE Computer Society, 14(1), 74–79.Google Scholar
  17. Magana, A. J., Falk, L. M., & Reese, J. M. (2013). Introducing Discipline-Based Computing in Undergraduate Engineering Education. ACM Transactions on Computing Education, 13(4), 1–22.Google Scholar
  18. Magana, A. J., Falk, M. L., Vieira, C., & Reese, M. J. (2016). A case study of undergraduate engineering students' computational literacy and self-beliefs about computing in the context of authentic practices. Computers in Human Behavior 61, 427–442.
  19. Mansbach, R., Ferguson, A., Kilian, K., Krogstad, J., Leal, C., Schleife, A., et al. (2016). Reforming an undergraduate materials science curriculum with computational modules. J Mater Educ, 38(3–4), 161–174.Google Scholar
  20. Mulop, N., Yusof, K. M., & Tasir, Z. (2012). A review on enhancing the teaching and learning of thermodynamics. Procedia-Social and Behavioral Sciences, 56, 703–712. Scholar
  21. National Science Foundation. (2011). Empowering the nation through discovery and innovation - NSF strategic plan for fiscal years (FY) 2011–2016. Washington, DC: Author. Retrieved from
  22. Olds, B. M., Streveler, R. A., Miller, R. L., & Nelson, M. A. (2004). Preliminary results from the development of a concept inventory in thermal and transport scienceage, 9, 1.Google Scholar
  23. PITAC. (2005). Computational science: ensuring America’s competitiveness. Retrieved from
  24. Posner, G. J., Strike, K. A., Hewson, P. W., & Gertzog, W. A. (1982). Accommodation of a scientific conception. Toward a theory of conceptual change. Sci Educ, 66(2), 211–227. Scholar
  25. Probst, D. K., &Zhang, Y. (2013) A gentle bridge between dynamics and thermodynamics. In proceedings of the 2013 ASEE Annual Conference & Exposition, Atlanta, Georgia.
  26. Pyatt, K., & Sims, R. (2012). Virtual and physical experimentation in inquiry-based science labs: attitudes, performance and access. J Sci Educ Technol, 21(1), 133–147. Scholar
  27. Rubin, A. (2012). Statistics for evidence-based practice and evaluation (research, statistics and program evaluation) (3rd ed.). Nashville: Cengage Learning.Google Scholar
  28. Shiflet, A. B., & Shiflet, G. W. (2006). Introduction to computational science: Modeling and simulation for the sciences. Princeton: Princeton University Press.Google Scholar
  29. The Joint Task Force on Computing Curricula—ACM/IEEE-Computer Society. (2013). Computer Science Curricula 2013: Curriculum Guidelines for Undergraduate Degree Programs in Computer Science. Practice.
  30. The Minerals, Metals & Materials Society. (2013). Integrated Computational Materials Engineering (ICME): implementing ICME in the aerospace, automotive, and maritime industries. Warrendale. Retrieved from
  31. Turbak, F., & Berg, R. (2002). Robotic design studio: Exploring the big ideas of engineering in a liberal arts environment. J Sci Educ Technol, 11(3), 237–253. Scholar
  32. Turner, P. R., Cunningham, S., Phillips, A. T., Claire, E., Shiflet, A. B., Stewart, K., …Shiflet, A. (2002). Undergraduate Computational Science and Engineering Programs and Courses. In SIGCSE (pp. 96–97). Covington, Kentucky.
  33. Vieira, C., Magana, A. J., Falk, M. L., & Garcia, R. E. (2017). Writing in-code comments to self-explain in computational science and engineering education. ACM Transactions on Computing Education, 17(4), 1–21.
  34. Weintrop, D., Beheshti, E., Horn, M., Orton, K., Jona, K., Trouille, L., & Wilensky, U. (2016). Defining computational thinking for mathematics and science classrooms. J Sci Educ Technol, 25(1), 127–147. Scholar
  35. Windschitl, M. A. (1995). Using computer simulations to enhance conceptual change: the roles of constructivist instruction and student epistemological beliefs. (Doctor of Philosophy), Iowa State University Ames, Iowa.Google Scholar
  36. Wofford, J. (2009). K-16 computationally rich science education: A ten-year review of the journal of science education and technology (1998–2008). J Sci Educ Technol, 18(1), 29–36. Scholar
  37. Yang, D., Streveler, R. A., Miller, R. L., Slotta, J. D., Matusovich, H. M., & Magana, A. J. (2012). Using computer-based online learning modules to promote conceptual change: helping students understand difficult concepts in thermal and transport science. Int J Eng Educ, 28(3), 686.Google Scholar

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© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Computer and Information TechnologyPurdue UniversityWest LafayetteUSA
  2. 2.School of Materials EngineeringPurdue University, Neil Armstrong Hall of EngineeringWest LafayetteUSA

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