Researching Outcomes

The Problem

Research based on small datasets

Educational research:

• Small datasets inform curriculum and instructional practice

• Missing granular and realtime information from classrooms

• Observational studies are costly and can be biased

Solutions Do Not Scale

The research base on instructional practices—and how curriculum is being implemented—is surprisingly thin. Due to lack of large datasets, researchers often rely on administrative data rather than granular information from actual classrooms in real time. This creates a significant “blind spot” about what solutions may—or may not—be working at scale.

Petrilli, M.J. (2018). Big Data Transforms Education Research: Can machine learning unlock the keys to great teaching? Education Next, 18(1), 86-87

OUR SOLUTION

We believe large datasets are more likely to be representative of the population as a whole and the specific issue being studied.

While smaller research studies provide important qualitative insights, we believe larger datasets lead to more generalizable, reliable, and ultimately scalable solutions.

Our research partners are working with us to assure we report the most critical and reliable information for researchers possible. Our commitment is to provide appropriately anonymized data—free or at cost—through our L&L Research Partnership Program.

Data that informs innovation and supports scale