EarSketch: An Authentic, Studio-Based STEAM Approach to High School Computing Education

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This project will study the influence on positive student achievement and engagement (particularly among populations traditionally under-represented in computer science) of an intervention that integrates a computational music remixing tool -EarSketch- with the Computer Science Principles, a view of computing literacy that is emerging as a new standard for Advanced Placement and other high school computer science courses. The project is grounded on the premise that EarSketch, a STEM + Art (STEAM) learning environment, embodies authenticity (i.e., its cultural and industry relevance in both arts and STEM domains), along with a context that facilitates communication and collaboration among students (i.e., through a studio-based learning approach). These elements are critical to achieving successful outcomes across diverse student populations. Using agent-based modeling, the research team will investigate what factors enhance or impede implementation of authentic STEAM tools in different school settings. The researchers will be engaged in a multi-stage process to develop: a) an implementation-ready, web-based EarSketch learning environment that integrates programming, digital audio workstation, curriculum, audio loop library, and social sharing features, along with studio-based learning functionality to support student presentation, critique, discussion, and collaboration; and b) an online professional learning course for teachers adopting EarSketch in Computer Science Principles courses. Using these resources, the team will conduct a quasi-experimental study of EarSketch in Computer Science Principles high school courses across the state of Georgia; measure student learning and engagement across multiple demographic categories; and determine to what extent an EarSketch-based CS Principles course promotes student achievement and engagement across different student populations. The project will include measures of student performance, creativity, collaboration, and communication in student programming tasks to determine the extent to which studio-based learning in EarSketch promotes success in these important areas. An agent-based modeling framework in multiple school settings will be developed to determine what factors enhance or impede implementation of EarSketch under conditions of routine practice.