Helikar, R. | bioRxiv Science

bioRxiv2022

DBERlibR: Automated Data Analysis for Discipline-Based Education Research

Discipline-Based Education Research (DBER) scientists repeatedly analyze assessment data to ensure question items reliability and examine the efficacy of a new educational intervention. Analyzing assessment data comprises multiple steps and statistical techniques that consume much of researchers time and are error-prone. While education research continues to grow across many disciplines of science, technology, engineering, and mathematics (STEM), the DBER community lacks tools to streamline education research data analysis. DBERlibR--an R package to streamline and automate DBER data processing and analysis--fills this gap. The package reads user-provided assessment data, cleans them, merges multiple datasets (as necessary), checks assumption(s) for specific statistical techniques (as necessary), applies various statistical tests (e.g., one-way analysis of covariance, one-way repeated-measures analysis of variance), and presents and interprets the results all at once. By providing the most frequently used analytic techniques, this package will contribute to DBER by facilitating the creation and widespread use of evidence-based knowledge and practices. The outputs contain a sample interpretation of the results for users convenience. User inputs are minimal; they only need to prepare the data files as instructed and type a function in RStudio to conduct a specific data analysis.

scientific communication and education↗

bioRxiv2020

Using computational modeling to teach metabolism as a dynamic system improves student performance

Understanding metabolic function requires knowledge of the dynamics, interdependence, and regulation of biochemical networks. However, current approaches are not optimal to develop the needed mechanistic understanding, and misconceptions about biological processes persist even after graduation. To address these issues, we developed a computational modeling and simulation approach that employs scaffolded learning to teach biochemistry students about the regulation of metabolism. The power of the approach lies in students abilities to alter any component or connection in a modeled system and instantly observe the effects of their changes. We find that students who use our approach perform better on biochemistry metabolism questions compared to students in a course that did not use this approach. We also investigated performance by gender and found that our modules may have the potential to increase equity in education. We noted that students are generally positive about the approach and appreciate its benefits. Our modules provide life science instructors with a dynamic and systems-driven approach to teach metabolic regulation and control that improves learning and also equips students with important technical skills.

scientific communication and education↗