Scalable self-paced e-learning of statistical programming with fine-grained feedback and assessment

Oregon Convention Center 

Assessing statistical programming skills consistently and at scale is challenging. Much like writing style is assessed in essay tasks, discriminating code quality and style from code function or output is becoming increasingly important as students adopt code-generating tools such as ChatGPT. In many cases checking code output alone is insufficient to assess students' understanding and ability to write statistical code. Instead, instructors often need to check the code itself for evidence of computational thinking, such as the use of appropriate functions, data structures, and comments. Unfortunately, manual review of code is time-consuming and subjective, and the skills needed to automate this process are complex to learn and use. In this talk, we introduce a new approach to authoring self-paced interactive modules for learning statistics with R. It is built using Quarto and WebR, leveraging literate programming to quickly create exercises and automate assessments. We discuss how this format can be used to write assessments with automated checking of multi-choice quizzes, code input and outputs, and the advantages of in-browser execution via WebR compared to existing server-based solutions.