An Instrument Assembly and Data Science Lab for Early Undergraduate Education

• Alison Wallum ^[3] ; Zetai Liu ^[3] ; Joy Lee ^[3] ; Subhojyoti Chatterjee ^[1] ; Lawrence Tauzin ^[1] ; Christopher D. Barr ^[1] ; Amberle Browne ^[2] ; Christy F. Landes ^[1] ; Amy L. Nicely ^[2] ; Martin Gruebele ^[3]
  1. [1] Rice University

     Rice University

     Estados Unidos

     
  2. [2] Danville Area Community College

     Danville Area Community College

     Township of Danville, Estados Unidos

     
  3. [3] University of Illinois at Urbana−Champaign, United States

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• Localización: Journal of chemical education, ISSN 0021-9584, Vol. 100, Nº 5, 2023, págs. 1866-1876
• Idioma: inglés
• Texto completo no disponible (Saber más ...)
• Resumen

  • As data science and instrumentation become key practices in common careers ranging from medicine to agriscience, chemistry as a core introductory course must introduce such topics to students early and at an accessible level. Advanced data acquisition and data science generally require expensive precision instrumentation and massive computation, often out-of-reach even for upper-level undergraduate laboratory courses. At the same time, a new generation of affordable do-it-yourself instruments presents an opportunity for incorporation of curricula focused on instrument design and computation into freshman-level courses. We present a new lab for integration into existing courses that starts with hands-on spectrometer building, moves to data collection, and finally introduces an advanced data science technique, singular value decomposition, at an appropriate level with minimal computing requirements. The hardware and software used are modular and inexpensive. The lab was tested in three community college general chemistry sections over two semesters. Previously, students taking these courses did not typically see advanced quantitative chemistry curricula before deciding whether to pursue a bachelor’s degree. This lab allowed students to practice data collection and organization skills, use prewritten Jupyter notebooks that perform advanced data analysis, and gain presentation skills. A multiwave assessment completed by students highlights both successes and difficulties associated with incorporating multiple advanced topics involving instrument design, data collection, and analysis techniques in a single lab.