The mission of the National Tutoring Observatory is to improve teaching and learning at scale by learning from great tutors. We observe and record tutors at work in one-on-one and small group interactions with learners. By partnering with a range of tutoring providers, we are creating the world's largest data repository of tutoring interactions and the incredibly important work of teachers. We are building the Million Tutor Moves dataset that records at least one million interactions between teachers and students across a range of subjects, grade levels, and educational contexts.
This new resource will bring:
New insight into the craft of teaching that can help train teachers
Advances in the science of instruction
Important data used by technologists to develop AI tools
open-source infrastructure
In addition to building the world’s largest tutoring dataset, the National Tutoring Observatory develops open-source, modular research infrastructure to safely and securely process tutoring data. Our growing GitHub repository provides tools that integrate directly into the workflows of researchers, tutoring providers, and education stakeholders seeking insight from their data. These tools include applications for annotating tutoring moves using custom codebooks, de-identifying sensitive data, and processing multimodal data such as chat, whiteboard activity, video, and transcripts.
The National Tutoring Observatory is committed to fostering equitable access to high-quality data and tools, especially for underserved communities most affected by pandemic-related learning loss. By partnering with tutoring organizations,school districts, and other education providers, we develop evidence-based insights into effective teaching strategies. These findings are shared through training workshops, open-source tools, and open community meetings, ensuring their practical application benefits educators and students alike.
Check out our team of researchers, educators, and technologists.
This material is based upon initial work completed under National Science Foundation Grant No. 2321499, and support from the Gates Foundation and the Chan Zuckerberg Initiative. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the funders.
