Authors:
Changsoo Song, Tomas Helikar, Wendy Smith, & Resa Helikar
Title:
Automated Data Analysis of STEM Education Interventions with DBERlibR
Abstract:
DBER scientists pursue evidence-based knowledge and practices to enhance teaching and learning in the STEM disciplines by collecting, integrating, cleaning, and analyzing multi-modal data. The pursuit of evidence-based practices entails continuous development and testing of different learning approaches and repeated collection and analysis of examination/test data. However, it is a daunting task to clean test data and perform statistical analyses, particularly when many DBER faculty were trained in a STEM discipline and not as education researchers. These data-heavy processes require multiple steps, some of which are error-prone and time-consuming, potentially leading to limited study reproducibility. For example, researchers need to merge and/or bind the data, test assumptions required for parametric techniques, and/or employ non-parametric techniques as necessary. Researchers handle these processes individually using multiple packages/functions because the current statistical data analysis tools are typically a “buffet-style” that requires users to select each individual analysis to run. Helikar Lab at UNL has developed DBERlibR - an R package to streamline and automate DBER data processing and analysis. The R package reads user-provided data, cleans them, merges/binds multiple data sets (as necessary), applies various statistical techniques to check assumptions (as necessary) and run the main analysis. and presents and interprets the results for users automatically and all at once.
Time: Thursday, 4/28/2022, from 2:00 - 3:00 p.m.
Zoom Link: https://unl.zoom.us/j/212107342