Higher education's dual mission of research and teaching position the sector to rapidly discover and deploy new processes for teaching and learning. However, resource constraints and traditional structures in higher education can pose major barriers.
Summary
The author of this report argues that a new academic role, the learning engineer, is needed to bridge the gap between learning research and teaching practice. Learning engineers can help design new learning environments and data systems that support course improvement, capture instructor insights, and collect student feedback. Further, they can aid in selecting knowledge modeling approaches for specific students, contexts and learning goals. In short, learning engineers can facilitate rapid progress in the basic science of human learning.
Key Insights
- Calls for accountability on the part of higher education have given rise to fundamental questions about how people learn and how one knows when learning is happening.
- Results from the science of learning can help resolve controversies about common education practices based on ideology and opinion. Yet these results rarely have translated into successful changes in teaching practice and student learning.
- Students most in need of a robust personalized academic support system often are enrolled at institutions with the most resource constraints.
- Across sectors, advances in machine learning, data science, crowdsourcing and computation are enabling more human processes and decision making to be done by machines, which are becoming essential to the teaching process in higher education.
