This project builds on prior efforts to create teaching resources for high-school Advanced Placement Statistics teachers to use an open source statistics programming language called "R" in their classrooms. The project brings together datasets from a variety of STEM domains, and will develop exercises and assessments to teach students how to program in R and learn the underlying statistics concepts.
Increasingly, all STEM fields rely on being able to understand data and use statistics. This project builds on prior efforts to create teaching resources for high-school Advanced Placement Statistics teachers to use an open source statistics programming language called "R" in their classrooms. The project brings together datasets from a variety of STEM domains, and will develop exercises and assessments to teach students how to program in R and learn the underlying statistics concepts. Thus, this project attempts to help students learn coding, statistics, and STEM simultaneously in the context of AP Stats. In addition, researchers will examine the extent to which students learn statistical concepts, computational fluency, and critical reasoning skills better with the online tools.
The resources developed by the project aim to enhance statistics learning through an integrated application of strategies previously documented to be effective: a focus on data visualization and representation, engaging students in meaningful investigations with complex real-world data sets, utilizing computational tools and techniques to analyze data, and better preparing educators for the needs of a more complex and technologically-rich mathematical landscape. This project will unite these lines of work into one streamlined pedagogical environment called CodeR4STATS with three kinds of resources: computing resources, datasets, and assessment resources. Computing resources will include freely available access to an instance of the cloud-based R-studio with custom help pages. Data resources will include over 800 scientific datasets from Woods Hole Oceanographic Institute, Harvard University's Institute for Quantitative Social Science, Hubbard Brook Experimental Forest, Boston University, and Tufts University with several highlighted in case studies for students; these will be searchable within the online environment. Assessment and tutoring resources will be provided using the tutoring platform ASSISTments which uses example tracing to provide assessment, feedback, and tailored instruction. Teacher training and a teacher online discussion board will also be provided. Bringing these resources together will be programming lab activities, five real-world case studies, and sixteen statistics assignments linked to common core math standards. Researchers will use classroom observational case studies from three classrooms over two years, including cross-case comparison of lessons in the computational environment versus offline lessons; student and teacher interviews; and an analysis of learner data from the online system, especially the ASSISTments-based assessment data. This research will examine learning outcomes and help refine design principles for statistics learning environments.