Reengineering Education: Systems Engineering and the LearningGraph as a Means to Developing a Coherent Learning Data Architecture

COMMITTEE

Eric Klopfer
Professor and Director, Scheller Teacher Education Program
Department of Comparative Media Studies

Mitchel Resnick
LEGO Papert Professor of Learning
Research Director, Lifelong Kindergarten Group, MIT Media Lab

W. Danny Hillis
Visiting Professor / Co-Founder
MIT Media Lab / Applied Minds & Applied Invention

Jeremy Roschelle
Executive Director, Learning Sciences Research
Digital Promise Global

ABSTRACT
Today’s educational systems are complex, political, sociotechnical ecosystems that struggle to meet the needs of most learners and societal demands—and most critically, struggle to change. Yet, learners globally need access to high quality learning environments and coherent learning pathways that support them to thrive in our complex world. Fundamental to every learning technology, environment, and system, is a learning data model and architecture that helps to facilitate the learner’s experience. To date, in traditional educational systems, this has largely been dominated by public policy curriculum standards, which have tremendous limitations and shortcomings on classroom practice and their ability to support complex learning technologies. At the same time, over the past several decades tremendous advances have been made in the learning sciences, learning analytics, and learning technologies that have greatly expanded our ability to model learning and provide immersive and adaptive learning environments. Yet each of these communities rarely coordinate and align these data models. The disjointedness of these structures leaves their architecture in a messy, challenging state, unable to successfully carry us into an advanced future of learning technologies and effective learning ecosystems.

This dissertation explores the use of Systems Engineering as a means to reengineering this critical pillar of the system, through the LearningGraph—a research initiative that used this methodology to create a unified data structure for modeling learning constructs in a coherent learning data architecture. The aim of the project is to ultimately inform a new infrastructure to support learning development across learning technologies and environments. In doing so, we create the foundation for closing tremendous gaps in the current system: between learning sciences research and practice; curriculum and assessment design; the design of learning technologies and all the aforementioned components; and between and across education systems globally. Moreover, it creates the potential for the foundation of a very different future for learning ecosystems.