Optimal Learning Lab

Applying cognitive theory to optimize learning.
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NSF Grant Detail

Improving STEM K-12 Learning Using Optimal Spaced Retrieval in an Existing Educational Technology Platform

This NSF-supported project focuses on using optimal spaced retrieval inside an existing educational technology platform so that STEM students remember more of what they learn over time instead of forgetting material soon after instruction.

Summary

Project summary

The project's goal is to build a study and review tool that helps students retain science and mathematics learning over longer periods by using optimal spaced retrieval. A key idea is that the system should adapt to the amount of time students actually have available for practice, rather than overwhelming them with rigid review schedules.

Within the context of this site, the project extends the lab's long-running interest in adaptive practice and memory-based scheduling into K-12 STEM learning using an existing educational technology platform.

Team

Named collaborators

Principal investigator

  • Paulo Carvalho, Carnegie Mellon University

Co-investigators and project team

  • Philip Pavlik, University of Memphis
  • Joshua Ling, Podsie
  • Meng Cao, postdoctoral research fellow
  • Yumou (James) Wei, PhD student

Site role

This grant brings the lab's spacing and retrieval ideas into a K-12 STEM setting where practical time constraints and classroom realities matter.
That makes it a strong bridge between theory, platform design, and real instructional use.