The Technology for Effective and Efficient Learning (TEEL) Lab at Carnegie Mellon University
focuses on research in learning methods, technology for learning systems, curriculum
development, and workforce training. The ultimate goal is to forge a path for lifelong learning
and career ever-readiness through innovative bridging of learning between formal education,
informal education, and the workplace.
The TEEL Lab develops learning platforms that support learning through discussion and
collaboration, focusing on hands-on technology areas such as cloud computing, machine learning,
and data science. The target interoperable online learning ecosystem under development by the
team is designed to enable effective and efficient learning through iterative and interactive
learning coupled with contextualized and timely feedback, and by leveraging social interactions
between students as a substantial learning resource.
The lab conducts research studies on student learning and evaluates innovative approaches for
incorporating social and interactive learning as a driver for developing cognitive and
meta-cognitive skills and motivation. Its work is based on research spanning
more than a decade in Computer Science Education, Software Engineering, Learning
Sciences, and Language Technologies.
In addition to scientific findings, technical advances, and principles for instructional design,
the team’s past research has also produced open source resources to
share with the broader community. Specific features of the in-progress LMS-pluggable online
course infrastructure is that it enables project-based learning through auto-graded projects
that provide contextualized feedback. We also incorporate social learning in the form of project
reflection and feedback cycles after each project, timed and role-based online learning
exercises and rubric-driven peer code-review.
Members of the TEEL Lab collaborate with other groups at CMU, other
universities, community colleges as well as industry. Find out how you can get involved from our
Call for Participation.
Funded in part by a grant from Microsoft and grants NSF grants ACI-1443068, IIS 1822831 and IIS