Added detailed simulation results showing how calendar half-life
translates to effective working-time decay:
- 10h/day (intensive): 72h -> 28.5 work hours at 50%
- 8h/day (typical): 72h -> 22.8 work hours at 50%
- 4h/day (part-time): 72h -> 11.5 work hours at 50%
Key finding: 72h calendar half-life consistently yields ~2.9 work
days at the 50% point, regardless of daily work hours. Activity
scores correctly reflect work fraction (8h/24h = 33.3%).
Explains why 72h calendar half-life was chosen:
- Decay applies to wall-clock time, not working time
- Users work ~8h/day (1/3 of 24h period)
- 72h calendar = ~24h of working time = one full workday
- Prevents overnight breaks from penalizing scores
This calibrates decay to actual engagement patterns rather
than raw calendar time.
Changed JUPYTERHUB_ACTIVITYMON_HALF_LIFE default from 48h to 72h
for more stable activity scores. Activity from 3 days ago now has
50% weight, better suited for users with irregular schedules.
Updated: Dockerfile, custom_handlers.py, activity_sampler.py,
settings_dictionary.yml, README.md, docs/activity-tracking-methodology.md
Research document covering industry approaches for activity tracking:
- Exponential Moving Average (EMA) with half-life decay
- Hubstaff's hour-based approach
- Daily target method (8h=100%)
- GitHub contribution graph methodology
Validates current EMA implementation aligns with industry standards.
