Ten simple rules for teaching an introduction to R
Year: 2024
Type: article
Abstract: The demand for data science skills across disciplines has led to massive growth of data-related programs in universities, standalone courses, and other learning materials [1].While demand is high, program administrators often struggle to find instructors [2].Many learners end up teaching themselves and climbing a very steep learning curve [3].On the other hand, scientists with deep research backgrounds often end up teaching with little experience or guidance.We believe better resources for busy and/or less experienced instructors are part of the solution to meeting the demand for programming skills education.The Open Educational Resources (OER) movement has propelled data science forward by improving the quality, depth, and availability of free materials [4].This includes textbooks, tutorials, vignettes, datasets, software tools, case studies, and of course, programming courses.Despite ongoing challenges, such as awareness and access to computers, democratizing educational resources makes high-quality content available to a global audience.This in turn nurtures collaboration and innovation.As data science continues to influence all aspects of our lives, we must consider key takeaways for adopting materials across classrooms and contexts.Here, we provide guidance to instructors tasked with teaching beginner level programming, specifically an introduction to the R language.Our perspective comes from co-teaching over 300 professionals, graduate students, and undergraduate interns at institutions like Johns Hopkins School of Public Health, University of Washington, and Fred Hutchinson Cancer Center.Our learners usually lack a computer science background and often juggle full-time employment and coursework.The 10 rules we present distill our key reflections from our experiences teaching R. We hope these rules will help you plan your course structure, incorporate general guidance and approaches, and understand specific pitfalls learners are likely to encounter. Rule 1: Make it intensiveMost of us feel the strain of day-to-day distractions like responding to and reading emails, Slack messages, and various other pings.These forms of communication, while (probably) important, can be distracting and get in the way of deep work [5].Programming is about problem solving, which can be frustrating or even impossible if you are thinking about the next task on your to-do list.Instead of learning R in small chunks, we suggest a condensed format for introducing R where learners' distractions are minimized.Intensive courses can lead to better learner experiences and outcomes, such as greater course effectiveness [6], lower perceived stress [7], improved focus [7,8], and greater student success rates [8].As the instructor, you should also fully commit to being present for learners by minimizing meetings and unrelated correspondence where possible during a condensed course.
Source: PLoS Computational Biology
Authors: Ava M. Hoffman, Carrie Wright
Institutions: Johns Hopkins University, Fred Hutch Cancer Center
Language: English
Subfield: Computer Science Applications
Field: Computer Science
Domain: Physical Sciences
SDG: Quality education
Open Access status: gold
Estimated APC paid: $2,926
OpenAlex ID: W4396949320