2023 ASEE Annual Conference & Exposition

Integrating programming-based modules into a materials characterization laboratory course to reinforce data science and scientific writing

Presented at Materials Division (MATS) Technical Session 1

The interdisciplinary nature of materials science and engineering (MSE) asks undergraduate majors in MSE to develop materials science domain knowledge and complementary skills such as data science (DS) and scientific writing (SW). With little room to pack additional courses into MSE curricula, better integration of these transferable skills into existing courses will help train our students to succeed in the modern workforce. This Work in Progress details the development of a series of programming-based modules to complement the data analysis in a materials characterization laboratory course. We use the Jupyter Book software to design a scaffolded series of Python-based exercises that focus primarily on data visualization, with additional exercises on tabular data analysis, curve fitting, and image processing. We administer pre- and post-course surveys to assess the impact of these modules on student learning and measure changes in student perception of the importance of these skills in MSE.

Authors
  1. Enze Chen Orcid 16x16http://orcid.org/0000-0002-7621-115X University of California, Berkeley [biography]
  2. Dr. Mark Asta University of California, Berkeley [biography]
  3. Andrew Minor University of California, Berkeley and Larwrence Berkeley National Laboratory [biography]
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