Make Teaching R in Undergraduate Biology Less Excruciating
We are no longer accepting applications for this opportunity. For future opportunities, please sign up for the QUBES Newsletter!
In this FMN, participants will focus on developing, implementing, and sharing modules for teaching statistical and biological concepts in R with Swirl, an interactive platform for learning and teaching R in the RStudio console. Swirl lessons simplify the R learning process by providing a guided, interactive experience through on-screen prompts and exercises which students answer directly in R. Swirl lessons can incorporate diverse biological datasets and can be used to seamlessly integrate learning of biology content, programming, and data analysis. The associated Swirlify package features a user-friendly shiny app for developing custom lessons. Over the course of the nine-week FMN, participants will be introduced to the Swirl program, implement an existing Swirl lesson to teach a biological or data analysis concept in their undergraduate biology course, and develop and implement a new Swirl lesson customized to their course needs. Participation in the course includes access to a collection of Swirl modules covering undergraduate biostatistics themes including data visualization, hypothesis testing, and regression, as well as user-contributed modules developed in this FMN. Participants will contribute one new lesson and will leave the FMN with more ready-to-use Swirl lessons covering diverse biology and data analysis concepts. This FMN is intended for undergraduate biology instructors with prior R programming experience who are interested in learning ways to teach R effectively to students with little to no programming experience. Participants should have at least a basic working knowledge of R, be comfortable performing basic data analysis operations in R including reading in and manipulating data, plotting data, performing t-tests and ANOVA, and constructing linear regression models. Individuals with no prior experience in R are unlikely to benefit significantly from participation in this FMN.
Dates and Location:
A 2-hour online kick-off will take place in late January, 2020 (date and time TBD). The faculty mentoring network will continue online for six weeks to support the development and implementation of activities in your course during the Spring 2019 semester. Participants will meet virtually for ~1 hour each week during for the first three weeks after the kick-off meeting, followed by three more meetings every other week.
To qualify, participants must be willing to implement one existing Swirl module into their course(s) during the Spring 2019 semester, and to create, implement, and share a new, custom Swirl module in the same semester. These new modules can be brief and should emphasize a topic relevant to the participant’s course goals. Participants must also be able to commit ~1 hour per week for working with the facilitator and collaborating with other participants around the customization and implementation of the teaching materials. Additional time outside of these discussions may be required for independent work on adapting and developing modules. Participants must also have experience with R and should be comfortable performing basic data analysis operations in R including reading in and manipulating data, plotting data, performing t-tests and ANOVA, and constructing linear regression models.
Benefits of Participation:
*Access ready-to-use teaching modules. Participants will gain access to an existing collection of Swirl courses designed for an undergraduate biostatistics course, as well as user-contributed Swirl lessons developed in this FMN.
*Online support throughout the process of implementing new materials in your course.
*Access to peer mentors on R, Swirl, and lecture/classroom/lab effective tips and strategies in online weekly meetings.
How to Apply:
Applications are due by December 6, 2019. Follow this link to view the application form. Accepted applicants will be notified by December 13, 2019. Space is limited; only 8 participants will be selected.