Hostname: page-component-586b7cd67f-gb8f7 Total loading time: 0 Render date: 2024-11-27T21:19:35.832Z Has data issue: false hasContentIssue false
  • English
  • Français

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

Published online by Cambridge University Press:  27 January 2020

Erik Einarsson Open the ORCID record for Erik Einarsson [Opens in a new window] ,
Olga Wodo ,
Prathima C. Nalam ,
Scott R. Broderick ,
Kristofer G. Reyes ,
E. Bruce Pitman  and
Krishna Rajan
Erik Einarsson
Affiliation:
Department of Materials Design and Innovation, The University at Buffalo, Buffalo, NY, USA
Olga Wodo
Affiliation:
Department of Materials Design and Innovation, The University at Buffalo, Buffalo, NY, USA
Prathima C. Nalam
Affiliation:
Department of Materials Design and Innovation, The University at Buffalo, Buffalo, NY, USA
Scott R. Broderick
Affiliation:
Department of Materials Design and Innovation, The University at Buffalo, Buffalo, NY, USA
Kristofer G. Reyes
Affiliation:
Department of Materials Design and Innovation, The University at Buffalo, Buffalo, NY, USA
E. Bruce Pitman
Affiliation:
Department of Materials Design and Innovation, The University at Buffalo, Buffalo, NY, USA
Krishna Rajan*
Affiliation:
Department of Materials Design and Innovation, The University at Buffalo, Buffalo, NY, USA
*
*(Email: [email protected])
Get access

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.

Keywords

informaticseducation
Type
Articles
Information
MRS Advances , Volume 5 , Issue 7: BROADER IMPACT , 2020 , pp. 293 - 303
Copyright
Copyright © Materials Research Society 2020

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Taber, K. S., Educational Studies 27 (2), 159–171 (2001).CrossRefGoogle Scholar
Taber, K. S., Science Education 89 (1), 94–116 (2005).CrossRefGoogle Scholar
Krause, S., Kelly, J., Tasooji, A., Corkins, J., Baker, D. and Purzer, S., International Journal of Engineering Education 26 (4), 869–879 (2010).Google Scholar
Rajan, K., in Informatics for Materials Science and Engineering, ed. Rajan, K. (Butterworth–Heinemann, Oxford, 2013), pp. 1–16.Google Scholar
Barabási, A. L., Nature Physics 8, 14–16 (2011).CrossRefGoogle Scholar
Son, L. K. and Simon, D. A., Educational Psychology Review 24 (3), 379–399 (2012).CrossRefGoogle Scholar
Jalili, M., Salehzadeh-Yazdi, A., Asgari, Y., Arab, S. S., Yaghmaie, M., Ghavamzadeh, A. and Alimoghaddam, K., PLoS ONE 10 (11), 1–8 (2015).Google Scholar
Bonacich, P., American Journal of Sociology 92 (5), 1170–1182 (1987).CrossRefGoogle Scholar
Rodríguez, J. A., Estrada, E. and Gutiérrez, A., Linear and Multilinear Algebra 55 (3), 293–302 (2007).CrossRefGoogle Scholar
Girvan, M. and Newman, M. E. J., Proceedings of the National Academy of Sciences 99 (12), 7821–7826 (2002).CrossRefGoogle Scholar
Csárdi, G. and Nepusz, T., InterJournal Complex Systems 2006 , 1695.Google Scholar
R Core Team, R: A Language and Environment for Statistical Computing, (R Foundation for Statistical Computing, Vienna, Austria, 2019).Google Scholar
Brandes, U. and Pich, C., Journal of Graph Algorithms and Applications 15 (1), 157–173 (2011).CrossRefGoogle Scholar
Chi, M. T. H., Topics in Cognitive Science 1 (1), 73–105 (2009).CrossRefGoogle Scholar