// module overview
This is the conceptual heart of the entire curriculum. A student with 85% attendance
isn't automatically fine — if they were at 98% three weeks ago, that 13-point drop is
the story. A student at 75% might not need urgent intervention if they've been steady
all semester. The delta tells you more than the current value ever can.
We'll build rolling attendance trends from raw daily records, learn to calculate week-over-week deltas, and create a simple early warning flag based on trajectory rather than threshold. This method is the foundation of everything else in the curriculum.
We'll build rolling attendance trends from raw daily records, learn to calculate week-over-week deltas, and create a simple early warning flag based on trajectory rather than threshold. This method is the foundation of everything else in the curriculum.
// key insight
Trajectory is more actionable than snapshot. A student whose attendance is declining needs a conversation today — not a referral next month when they've crossed a threshold.
// what you'll learn
What Educators Will Learn
- Why 'chronic absenteeism' thresholds catch problems too late — and what to use instead
- How to read a trend line: what constitutes a meaningful change vs. normal variation
- The difference between a student who is struggling and a student who is declining
- How early intervention timing changes outcomes — what the research says
- A framework for communicating trajectory data to parents and colleagues without alarm
Python Walkthrough
- Aggregating daily attendance records into weekly rates with
groupby()+resample() - Calculating rolling 4-week averages with
.rolling(window=4).mean() - Computing week-over-week deltas:
rate.diff()and what negative values mean - Visualizing individual student trajectories with matplotlib — adding a trend line
- Building a simple early warning flag: students with 3+ consecutive weeks of decline