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Data-driven visualization schema of a materials informatics curriculum: Convergence of materials science and information science

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Abstract

In addition to student assessment, curriculum assessment is a critical element to any pedagogy. It helps the educator assess the teaching of concepts, determine what may be lacking, and make changes for continual improvement. Meaningful assessment can be complicated when disciplines converge or when new approaches are implemented. To facilitate this, we present a network-based visualization schema to represent a materials informatics curriculum that combines materials science and data science concepts. We analyze the curriculum using network representations and relevant concepts from graph theory. This reveals established connections, linkages between materials science and data science, and the extent to which different concepts are connected. We also describe how some materials science topics are introduced from a data perspective, and present an illustrative case study from the curriculum.

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References

  1. K. S. Taber, Educational Studies 27 (2), 159–171 (2001).

    Article  Google Scholar 

  2. K. S. Taber, Science Education 89 (1), 94–116 (2005).

    Article  Google Scholar 

  3. S. Krause, J. Kelly, A. Tasooji, J. Corkins, D. Baker and S. Purzer, International Journal of Engineering Education 26 (4), 869–879 (2010).

    Google Scholar 

  4. K. Rajan, in Informatics for Materials Science and Engineering, ed. K. Rajan (Butterworth-Heinemann, Oxford, 2013), pp. 1–16.

  5. A. L. Barabási, Nature Physics 8, 14–16 (2011).

    Article  Google Scholar 

  6. L. K. Son and D. A. Simon, Educational Psychology Review 24 (3), 379–399 (2012).

    Article  Google Scholar 

  7. M. Jalili, A. Salehzadeh-Yazdi, Y. Asgari, S. S. Arab, M. Yaghmaie, A. Ghavamzadeh and K. Alimoghaddam, PLoS ONE 10 (11), 1–8 (2015).

    Article  Google Scholar 

  8. P. Bonacich, American Journal of Sociology 92 (5), 1170–1182 (1987).

    Article  Google Scholar 

  9. J. A. Rodríguez, E. Estrada and A. Gutiérrez, Linear and Multilinear Algebra 55 (3), 293–302 (2007).

    Article  Google Scholar 

  10. M. Girvan and M. E. J. Newman, Proceedings of the National Academy of Sciences 99 (12), 7821–7826 (2002).

    Article  CAS  Google Scholar 

  11. G. Csárdi and T. Nepusz, InterJournal Complex Systems 2006, 1695.

    Google Scholar 

  12. R Core Team, R: A Language and Environment for Statistical Computing, (R Foundation for Statistical Computing, Vienna, Austria, 2019).

    Google Scholar 

  13. U. Brandes and C. Pich, Journal of Graph Algorithms and Applications 15 (1), 157–173 (2011).

    Article  Google Scholar 

  14. M. T. H. Chi, Topics in Cognitive Science 1 (1), 73–105 (2009).

    Article  Google Scholar 

Download references

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Einarsson, E., Wodo, O., Nalam, P.C. et al. Data-driven visualization schema of a materials informatics curriculum: Convergence of materials science and information science. MRS Advances 5, 293–303 (2020). https://doi.org/10.1557/adv.2020.32

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  • DOI: https://doi.org/10.1557/adv.2020.32

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