Time to completion of web-based physics problems with tutoring.

Warnakulasooriya et al. (2007) watched how college students moved through online physics problems. They used the computer's built-in log to record every click and the time between clicks.

No teaching or reward system was added. The team simply described the speed patterns they saw.

Three clear groups showed up in the logs. One group raced to the end in a straight line, likely copying answers. A second group started fast then stalled, a sign of giving up. The last group took steady, thoughtful steps.

These time trails gave a quick red-flag alert about who needed help or honesty checks.

Tarifa-Rodriguez et al. (2024) and Tarifa-Rodriguez et al. (2023) both catalog similar log tricks for college coursework. Their reviews count clicks, time, and text to size up engagement, showing the 2007 idea is now a full tool kit.

Kim et al. (2014) used eye-tracker logs instead of click logs and also found slow, fast, and stuck profiles while students read graphs. Same concept, different sensor.

Ben-Yehudah et al. (2019) seems to clash at first: they say digital text hurts students with ADHD. But they measured comprehension, not speed. Rasil looked at time, Gal looked at understanding—two separate outcomes, no real fight.

You already collect response latency in discrete trials. Add a simple timer to any online task you give clients. A sudden drop or jump in seconds can signal cheating, fatigue, or mastery before the data sheet shows it. One glance at the log can guide your next prompt or honesty reminder without extra tests.