Using a Modular Approach to Introduce Python Coding to Support Existing Course Learning Outcomes in a Lower Division Analytical Chemistry Course
| Autor: | Eleanor Gillette, Julia A. Schafer, David O. De Haan |
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| Rok vydání: | 2021 |
| Předmět: |
Multimedia
Computer science business.industry Computer programming Analytical Chemistry (journal) General Chemistry Modular design Python (programming language) Division (mathematics) computer.software_genre Education Course (navigation) ComputingMilieux_COMPUTERSANDEDUCATION business Curriculum computer Coding (social sciences) computer.programming_language |
| Zdroj: | Journal of Chemical Education. 98:3245-3250 |
| ISSN: | 1938-1328 0021-9584 |
| DOI: | 10.1021/acs.jchemed.1c00456 |
| Popis: | There is an increasing need for research chemists and biochemists to have a basic familiarity with computer programming. Adding programming content to already crowded STEM undergraduate curricula, however, can be challenging. When programming content is introduced within the chemistry curriculum, it is most often incorporated into upper division courses, but students could benefit from earlier exposure. Here, we describe incorporating Python programming, using Jupyter notebooks, into a lower division analytical chemistry course in support of the existing learning outcomes of the course. By linking lab experiments with specific course learning outcomes, quantitative topics, and computer programming concepts, we have created a series of analytical chemistry lab modules that introduce students to Python coding without losing any of the traditional content of the course. Additionally, we show that this curriculum can be successfully implemented by faculty with limited coding experience. Assessment indicates that the Python-enhanced course makes students more positive about the applicability of quantitative skills in many areas, and increases their self-perception of their level of preparation for future careers. |
| Databáze: | OpenAIRE |
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