For many students, physical chemistry represents their most challenging courses within the major. The integration of topics covered in calculus, physics, and previous chemistry courses leaves many students feeling overwhelmed and under prepared when they walk in on the first day of class. Additionally, the material presented in physical chemistry courses is abstract and multidimensional which can make it difficult for students to comprehend the concepts. A frequent refrain from students is that they have trouble connecting the material in physical chemistry with topics that are relevant to them. In order to overcome these hurdles we have developed a series of computational guided inquiry (CGI) modules. These assignments make use of the programming language Python implemented with Jupyter Notebooks, which allows for self-contained assignments that feature detailed instructions and background information, tasks for students to complete, reflection questions to answer, and executable code all alongside each other. In this presentation I will describe a set of assignments which make use of real data collected as part of polar research efforts. Polar research, which plays a vital role in developing our understanding of Earth’s climate system, is difficult to share with undergraduate students. These assignments therefore serve multiple purposes, including making the physical chemistry course content relatable to modern day scientific research efforts, introducing students to polar data and improving their climate change knowledge, and developing valuable computational skills for a cohort of undergraduate STEM students who rarely take any computer science courses.

Integrating polar research into the physical chemistry classroom using Python based computational guided assignments

Macromolecular Chemistry: The Second Century

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ACS Spring 2020 National Meeting & Expo

The official archive and repository for all ACS Meetings & Expositions abstracts and select posters or presentations.