This project is iteratively designing, beta-testing, implementing, and evaluating a fully automated, sharable Workflow Visualization System (WVS) to help researchers and educators preserve, organize, and analyze data captured from multiple design iterations of complex cyberlearning research and development (R&D) efforts. The team is augmenting a widely used learning management system (Moodle), which has existing data collection algorithms, with new tools that produce visual, multi-layered representations of two complementary aspects of R&D efforts: (1) the design of complex educational interventions, including information about variables such as tasks, resources used, participant structures, and workspaces; and (2) the enacted implementations of these interventions, including data about learners’ completion of tasks or acquisition of resources and learner-produced data. The WVS is envisioned ultimately to support reflective teaching practice, sharing of adaptable successful interventions, and design-based research (DBR). The project team is also tackling significant problems regarding privacy and protecting the rights of human subjects in research that involves data mining from online sources. The intellectual merit of this project rests in the promise it offers to enable the educational research community to better document the implementation history of complex cyberlearning interventions, including their contexts, rationale, iterations and outcomes. Current efforts, including the foundational work on which this new project builds, rely on manual production of representations of such rich data. But what this new project offers is a means to automate this data visualization strategy by mining both design information and student performance data from Moodle sites, making it widely available and easy to use. The project is exercising its broader impact through its choice of Moodle, one of the leading open-source learning management systems, that has over fifty-thousand sites supporting approximately forty million users world-wide. The WVS’ open-source code also permits its use with other learning management systems. Most importantly, the project has the potential to transform how the broader learning sciences and indeed cyberlearning sciences communities engage in their work, both as individual projects and as a network of projects, through improved knowledge sharing and accelerated adoption of results and best practices.