PIs: Roy Pea, Shuchi Grover, Bryan Brown
Stanford University
Award Details

This workshop is funded through the “Dear Colleague Letter: Principles for the Design of Digital Science, Technology, Engineering, and Mathematics (STEM) Learning Environments (NSF 18-017).” The goal of this workshop is to articulate a transformative vision of future STEM learning for diverse learners across domains and settings. It forges a nexus among the emerging (a) sciences of learning, (b) assessment, and (c) big data to formulate frameworks and tools for designing STEM learning environments. Taking an equity-first approach for broadening participation through innovative designs, this project convenes interdisciplinary teams to produce a white paper proposing forward-looking digitally-augmented STEM environments that bridge formal and informal learning contexts and are responsive to the needs to every learner. The white paper will also articulate a future research agenda that could lead to new breakthroughs at the Human-Technology Frontier. The open-invitation design workshop, strategically located at Stanford University, and dissemination through a public website and community outreach activities at key conferences in which these scholarly communities convene will ensure broad awareness of and access to these models, tools, frameworks, design principles, and research priorities for educators, researchers, and analysts.

The workshop is designed to construct needed new collaborations with the learning sciences, psychometrics, and computer science to design integrative STEM learning environments with robust in-process measures of adaptive learning that address key aspects of deeper learning. It convenes innovators advancing the state-of-the-art in equity-focused, technology-enhanced STEM learning, educational data mining and learning analytics, and computational psychometrics, to develop innovative ways to design and scale for a future of integrated STEM learning in an era of big data. An infrastructure of generative new algorithms and knowledge models, psychometric models, and learner pathway models will emerge from project activities at the intersection of these disciplinary perspectives to transform learning and assessment designs by incorporating signals from multimodal learning analytics and software for multi-faceted measurement of academic competencies. The project scope will be guided by three questions: (1) How can learning environments for integrated STEM learning scale successful efforts across diverse student populations and bridge formal and informal learning contexts? (2) What innovative research methods, statistical techniques and modeling formalisms are necessary to embed theoretical models in data-driven computational approaches in order to capture, characterize and support causal claims about individual and team-based learning, especially for complex, multi-source streaming data? (3) How can multi-domain threaded learning progressions be created for integrated learning and assessment of STEM subjects?