Team-Based Learning for Scientific Computing and Automated Experimentation: Visualization of Colored Reactions

Autor/inn/en	Vargas, Santiago; Zamirpour, Siavash; Menon, Shreya; Rothman, Arielle; Häse, Florian; Tamayo-Mendoza, Teresa; Romero, Jonathan; Sim, Sukin; Menke, Tim; Aspuru-Guzik, Alán
Titel	Team-Based Learning for Scientific Computing and Automated Experimentation: Visualization of Colored Reactions
Quelle	In: Journal of Chemical Education, 97 (2020) 3, S.689-694 (6 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Zamirpour, Siavash) ORCID (Aspuru-Guzik, Alán)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0021-9584
Schlagwörter	Teamwork; Computer Uses in Education; Cooperative Learning; Automation; Science Experiments; Color; Chemistry; Skill Development; Science Laboratories; Undergraduate Students; Programming Languages; Computer Software; Data Analysis + Suchen Sie Ihr Suchwort? Computernutzung; Kooperatives Lernen; Colour; Farbbezeichnung; Farbe; Chemie; Kompetenzentwicklung; Qualifikationsentwicklung; Auswertung
Abstract	The increasing integration of software and automation in modern chemical laboratories prompts special emphasis on two important skills in the chemistry classroom. First, students need to learn the technical skills involved in modern scientific computing and automation. Second, applying these techniques in practice requires effective collaboration in teams. This work aims at developing a teaching module to help students gain both skills. In particular, we describe a modular and collaborative approach for introducing undergraduate students to scientific computing in the context of automated and autonomous chemical laboratories. Using online collaboration tools, students work in parallel teams to develop central components of an automated computer vision system that monitors color changes in ongoing chemical reactions. These components include three different aspects: image capture, communication, and data visualization. The image capture team collects and stores the images of the chemical reaction, the communication team processes the images, and the visualization team develops the tools for analyzing the processed image data. Using this educational framework, students built an open-source Python tool called AutoVis that enables the automated tracking of color and intensity changes in a liquid. The software is tested by simulating chemical reactions with dilute solutions of food coloring in water. It is shown that the system reliably tracks color and intensity, providing feedback to the experimentalist and enabling further computational analysis. Over the course of the project, students gain proficiency in scientific computing using Python and collaborate on software development using GitHub. In this way, they learn the role of software in chemical laboratories of the future. (As Provided).
Anmerkungen	Division of Chemical Education, Inc. and ACS Publications Division of the American Chemical Society. 1155 Sixteenth Street NW, Washington, DC 20036. Tel: 800-227-5558; Tel: 202-872-4600; e-mail: eic@jce.acs.org; Web site: http://pubs.acs.org/jchemeduc
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01