Teaching Fundamental Skills in Microsoft Excel to First-Year Students in Quantitative Analysis

Abstract

Despite their technological savvy, most students entering university lack the necessary computer skills to succeed in a quantitative analysis course, in which they are often expected to input, analyze, and plot results of experiments without any previous formal education in Microsoft Excel or similar programs. This lack of formal education results in increased anxiety, students spending large amounts of time using the process of "trial and error" to complete the assignments, and detracts from the students' learning of the chemistry. Microsoft Excel tutorials that were previously introduced have either been not specific to chemistry, require multiple assignments throughout the semester to acquire the necessary skills, or are designed for deprecated versions of the software. In this work, we present an argument for implementing a chemistry-specific, version-agnostic spreadsheet interactive laboratory exercise that uses basic, general chemistry concepts to have students explore and learn the computer skills that are necessary to succeed in a quantitative analysis course. Student feedback data indicate that students felt that the interactive spreadsheet lab allowed them to develop skills that they identified as necessary for success in the course as well as for their future careers.

A Master Class in Dimensional Analysis: the Universal Gas Constant

“…16,17 We use the ubiquitous Microsoft Excel spreadsheet program as an efficient method of organizing the large amount of data in a streamlined fashion. This program has been used in undergraduate instruction on various aspects of quantitative analysis [18][19][20][21][22][23][24][25] and a number of books have been written on its use as a computational tool in solving problems in the chemical sciences. [26][27][28][29] Surprisingly, however, no exercises are given on its possible use in fundamental dimensional analysis as described in this work.…”

Using Authentic Data from NSF’s Ocean Observatories Initiative in Undergraduate Teaching: An Invitation

“…Engaging students in active learning by modeling the scientific process using real-world data is a high-impact educational practice (Rubin and Abrams, 2015;O'Reilly et al, 2017;Deslauriers et al, 2019). Data explorations allow students to engage in academically complex and challenging activities that require conceptual thought through the validation of physical models (Resnick et al, 2018) and the procedural knowledge needed to produce an analytical result (Kastens et al, 2015).…”

“…Though students may initially be challenged by the "messiness" of real data (Ellwein et al, 2014), as they gain confidence with the mechanics of data analysis, they can use authentic, open access data sets to independently solve complex, unstructured questions (Carey et al, 2015;Kastens et al, 2015) and improve their understanding of the nature of science and the limitations of any data set (Lederman, 1992). Data-driven activities can also encourage peer learning through small group work (Springer et al, 1999;Thomas and Brown, 2011;Brame and Biel, 2015;Toven-Lindsey et al, 2015) and cultivate data skills needed for future careers in academic and workplace environments (Langen et al, 2014;Rubin and Abrams, 2015;Carey and Gougis, 2017). Even the use of a single data-driven activity can result in students being more com-ABSTRACT.…”

“…Despite the well-documented link between data-focused lessons and development of higher-level cognitive skills, data-focused activities are often lacking in undergraduate curricula (Brewer and Gross, 2003;Michener and Jones, 2012;Carey et al, 2015;Rubin and Abrams, 2015). Some models for teaching effectively with authentic data in the undergraduate classroom do exist within the geoscience education community and have been found to increase knowledge retention and self-efficacy (e.g., Teasdale et al, 2015).…”

Using Large Data Sets for Open-Ended Inquiry in Undergraduate Science Classrooms

“…The use of relatively small datasets is widespread across undergraduate classrooms, in part because this can facilitate inquiry-based activities that allow students to ask their own questions, design experiments or manipulate equipment, and generate and analyze their own data. These are important learning outcomes, but working with datasets that are limited in size or complexity does not give students the opportunity to practice data management, spreadsheet navigation skills, or hypothesis testing based on data -skills which are sorely needed (Rubin & Abrams, 2015;Strasser & Hampton, 2012). Students recognize that these small datasets are often not appropriate for drawing strong conclusions, and a common refrain in laboratory reports is "more research is needed."…”