Macrosystems EDDIE modules increase students' proficiency and confidence working with R in both face-to-face and virtual classrooms

Background/Question/Methods

There is a pressing need to expand quantitative and computational training in undergraduate ecology curricula. In response, the Macrosystems EDDIE (Environmental Data-Driven Inquiry & Exploration; MacrosystemsEDDIE.org) program has developed pre-packaged teaching modules that teach students ecological modeling, data analysis, and forecasting skills. Previous assessment of these modules after face-to-face (F2F) instruction has demonstrated that the Macrosystems EDDIE modules significantly increase students' self-reported confidence and proficiency working in the R environment, but it is unclear how the modality of teaching may influence students' experience completing module activities. Do students who complete challenging data analysis and modeling activities virtually exhibit similar gains as students receiving face-to-face instruction? To answer this question, we assessed a class of undergraduate ecology students who completed a Macrosystems EDDIE module in synchronous virtual format and compared their responses to students who completed the same course with F2F instruction in a previous year. All instruction was kept similar except that virtual student teams worked in Zoom breakout rooms instead of in person and completed module activities in the R Studio Cloud environment instead of R Studio downloaded onto their laptops.

Results/Conclusions

We observed similar gains in students' perceptions of their confidence and proficiency working with R in both virtual and F2F classrooms. On average, both the virtual and F2F students exhibited a gain of 0.4 to 0.8 Likert-scale points in their self-assessments after completing a Macrosystems EDDIE module. The median response of both virtual and F2F students after completing a module was "intermediate R proficiency, able to apply this tool independently to many types of assignments" in comparison to "basic R proficiency, able to handle simple applications of this tool to an assignment" before the module (p=0.05). Importantly, the virtual students' perceptions of their confidence working with R and the ease of using R also significantly increased (both p=0.001). Moreover, when asked which tool they would prefer to use for a collaborative ecological data analysis project, the proportion of virtual students who responded that they would use R, R Studio, or R Studio Cloud software significantly increased (59% to 81%, p=0.04) after completing a module, mirroring a decrease in their responses that included Excel. Overall, while our analysis is limited to just one course, our data suggest that Macrosystems EDDIE modules can support the growth of students' quantitative and computational skills in both virtual and F2F classrooms.