When we say PAL takes a “quantitative approach to education,” many are unsure what that means. Here are the two guiding principles of that approach:
- Data drives decisions: content, order, and pacing come from measured signals, not hunches.
- Orders of magnitude more data: More data doesn’t mean a few more quizzes per term. We continuously track signals across tasks, time, and context. We generate 300+ indicators while a student works on a single question. We even build our own hardware with purpose-built sensors to measure them.
Why this path? Because it performs better on what matters:
- Greater learning efficiency. Paths and content are personalized and continuously optimized using real-time signals.
- A longitudinal view of learning. Tracking spans subjects and years, in and out of school. Decisions reflect the whole arc, not a single class.
- Reproducible quality at scale. What works is captured in models and reused. This offers a path to reducing global inequities in educational access.
- Less bias. Measured signals beat personal preferences and anecdotes.
Quantitative does not mean without humans; it shifts educators’ roles. Teachers should spend more time on the work only humans can do. They notice motivation and frustration. They read the room. They track social dynamics that no sensors can see. These qualitative observations become structured data that improve and calibrate model outputs. With more time freed up, teachers can invest in building relationships, culture, and habits. Meanwhile, machines do what they’re best at: processing vast amounts of data quickly, testing alternatives, and optimizing consistently.
This approach reflects IntelleCo’s core belief: humans and machines together can do what neither can alone.