Successes & lessons learned in an undergraduate computational lab sequence for materials science & engineering

Abstract

In 2012, with a switch from quarters to semesters of instruction during the academic calendar year, the Materials Science & Engineering Department at The Ohio State University added a series of computational labs to the required undergraduate curriculum. Over the course of the next 4 academic years, the achievement of student outcomes and student feedback on the courses were monitored while minor changes were made to the curriculum. While student outcomes were generally achieved, student dissatisfaction with the course structure was high. In the 2016- 2017 academic year, several substantial changes were made to the sophomore and junior lab courses in response to this data. Curricular changes included an increased emphasis on pseudo-code development, routine reflection on assumptions and limitations of models used in lab meetings, and a move of the lectures and discussions to after the in-depth lab assignments. In addition, short modules on data analysis, elementary statistics, and linear algebra were included. Interestingly, student feedback revealed that a number of "problems" with the lab sequence stem from the perception that either computational thinking is not a relevant skill for a materials engineer, or that students were not in fact learning more than how to use a specific software package. To combat these factors and increase students' self-efficacy, a "marketing campaign" was implemented for these courses. The results of these five years of aggressively including computational modeling into the undergraduate materials science curriculum, including student perceptions and achievement before and after these changes, can provide valuable insight for any department interested in making similar changes.