ACM campuses use multiple statistical analysis platforms, each with its own advantages for teaching and research. However, supporting multiple platforms on a small campus is not cost-effective, makes cross-disciplinary communication difficult, and requires students to learn multiple methods to perform the same analysis.
This project facilitates the transition to a single program called R, leveraging work already done through FaCE funding while also capitalizing on our multidisciplinarity to integrate R resources across curriculum and ACM institutions.
Note: Content adapted from original proposal
R is a free, Windows/Mac compatible, platform that enables a wide variety of statistical and graphical techniques. We propose to:
- Form learning communities initially comprising faculty from several different departments at Beloit College
- Seek experts in R to help us learn and implement analysis methods
- Develop modules that will be shared among our departments, and
- Disseminate our materials and learning activities among our ACM colleagues.
This multi-phase collaboration begins at Beloit College among faculty in several departments/programs who will be trained by an external consultant and then workshop together during the year. We will then host a multi-campus conference and share our work with the ACM community.
This project has three primary goals which address challenges faced by ACM schools.
Improve equitable access to quantitative software while containing costs
Our CIO reports that the college spends thousands yearly to license at least three statistical analysis programs (JMP, SPSS, STATA). Students must use limited numbers of college-owned computers or purchase programs at their own expense. R is a free program that directly reduces barriers to accessibility and equity.
Advance students’ quantitative reasoning skills and train faculty to do so
Despite the variety of Q courses (quantitative reasoning requirement) offered on campus, few opportunities exist to share or collaborate around the teaching of quantitative skills. By fostering faculty collaboration around a single program, we will reduce the introductory barriers to student learning and encourage deeper understanding of the important quantitative concepts.
Strengthens the value of a liberal arts degree
The liberal arts are often questioned for our ability to prepare students for the workforce. R provides an opportunity to expose students to computer programming — a highly marketable skill. Proficiency in R better equips our students for success in graduate school, where the shift towards R is already occurring.
Our team will produce a set of materials that are accessible to faculty, tested in the classroom, and easily adaptable to a variety of disciplines by the end of the 2019-2020 academic year.
Fall 2018 | Hiring an External Consultant
We will host Dr. Israel del Toro (Lawrence University) to learn the essentials of coding and teaching in R.
Summer 2020 | Conference
We will host a conference featuring training sessions on R and the materials developed by the project team over the past year, as well as panels on selected topics for ACM faculty interested in teaching R.
January 2019-Ongoing| Website Development
We will develop a website for all ACM-affiliated faculty to develop and share new material in a standardized manner. We will also hold campus development sessions to discuss how graphing, writing code, and group development of materials can aid instructors of Q courses.
At our two-day Summer 2020 conference, we will share our materials and tools with ACM colleagues to share with their home institutions for implementation in their own courses and curricula. We plan to create a publicly available (or perhaps ACM only) web-repository for teaching materials, activities, and data sets.
Finally, we know we are not the first to introduce undergraduate students to R. However, we believe that we are on the leading edge of this effort and thus aspire to present our work at a conference and/or in a published article or workbook.
Outcomes and Significance
We envision one set of materials that focus on teaching R to undergraduates in general (load data, visualize and edit data, and create basic graphs and tables), and a second, domain or discipline specific set that teaches students the most commonly used techniques for their field.
Materials (lecture, PowerPoints, R code, worksheets, etc) will be available online for faculty from all disciplines to access and contribute to. Depending on the success of these materials in the classroom, we may also develop an open-source undergraduate methods textbook or workbook for R.
We anticipate that our work will enhance interdisciplinary and intergenerational collaborations among faculty at Beloit. We expect that this process may also lead to further development and strengthening of our Quantitative Reasoning curriculum.