Bridging the Gap between Instructional and Research Laboratories: Teaching Data Analysis Software Skills through the Manipulation of Original Research Data

Abstract

The gap between graduate research and introductory undergraduate teaching laboratories is often wide, but the development of teaching activities rooted within the research environment offers an opportunity for undergraduate students to have first-hand experience with research currently being conducted and for graduate students to develop communication and pedagogical skills. We describe the development, evaluation, and redesign of a Microsoft Excel-based activity where data analysis from contemporary research was adapted into a data analysis training exercise, requiring undergraduate students to engage with the data by performing a series of analytical tasks guided by commentary from the researcher. In addition to learning common data analysis techniques, this exercise provided an opportunity for undergraduate students to practice problem solving and collaboratively work through trouble-shooting. Throughout the activity development, multiple iterations allowed the authors to explore the impact of different pedagogical approaches including, but not limited to, student-prepared questions for the graduate student author that were used as a starting point for an in-class discussion of the research findings, techniques, considerations, and future goals. This conversation between general chemistry students and the graduate student researcher was coupled with an in-person laboratory tour or video introduction by the researcher. These activities humanized the research enterprise for the undergraduate students and serve as a model for future collaborations between research and instructional laboratories.

Designing a Hybrid Biopharmaceutical Laboratory Course to Enhance Content Flexibility and Access

“…Although the use of sample student data and research data for STEM laboratory classes has been demonstrated previously, − , the instructors noticed two major challenges associated with the use of mined data. First, the data were difficult to understand when it was not originally collected by students themselves.…”

Guided inquiry activity linking thermodynamic parameters of protein unfolding to structure using differential scanning fluorimetry data in the biophysical chemistry classroom

“…The learning cycle used in our activity was based on the cycle seen commonly in process oriented guided inquiry learning (POGIL) and involves a thorough exploration phase in which students are asked simple questions to draw their attention to key aspects of the model before building their knowledge with those observations [25]. Other instructional materials developed for the undergraduate biophysical chemistry course have focused on laboratory instruction [26][27][28][29]. Activities using the POGIL method, which incorporates the learning cycle similar to ours, have been developed for the biochemistry class [30][31][32], but none have been published specifically for the biophysical chemistry class to the best of our knowledge.…”

Hypothesis Tests and Exploratory Analysis Using R Commander and Factoshiny

“…Normally, statistical calculations and plotting are taught using spreadsheets. ,− For example, variances and means of two independent groups may be compared using the F test and t test, respectively. − However, in recent years, programming software such as MATLAB, ,− Python, − Octave, , and R project − have been used for these purposes. Chemometric approaches such as machine learning, , multivariate calibration, and pattern recognition − were taught using these programs.…”