Deploy, Observe, and Improve

When instructors adopt materials developed through the facility, we’ll be able to gather data on how students interact with them—and use that information to guide improvement.

This data will be stored and curated in DataShop, a long-standing NSF-supported platform that enables detailed tracking of student responses and behaviors. From there, researchers can use LearnSphere, a suite of tools for analyzing learning data, comparing models, and identifying what’s working (and what’s not).

Why Big Data Matters for Learning Research

Traditional studies often rely on pre- and post-tests, providing just two snapshots of learning. With instrumented courseware, we can do better: we can track learning as it happens, fitting growth models that separate initial knowledge from learning rate. This helps us:

• Understand not just whether students succeed, but how they get there
• Identify which kinds of instructional materials lead to faster learning
• Detect patterns of struggle or disengagement early

These kinds of insights are hard—or impossible—to get without a shared, data-rich infrastructure.

We’d Love Your Input

We want to make sure this facility can support a broad range of research questions and instructional goals. Your input will help us design the right tools and data structures.

• What kinds of questions would you want to answer with this system?
• What instructional or learning challenges are you hoping to study?
• What types of analysis would you need to support that work?
• Are there specific models, metrics, or visualizations that would help?

We welcome your thoughts! You can participate in the public discussion below or provide private feedback here.