Understand learning rhythms. Don't surveil students.
We're exploring privacy-preserving ways to help students and teachers see digital learning rhythms — so they can support attention and self-regulation. This page is a statement of intent and ethics, not a product you can buy yet.
The line between insight and surveillance.
Student activity monitoring has earned its scrutiny. So before any of this is built, the framing has to be right — because the wrong one quietly becomes a watchlist.
"Install this on student devices so schools can monitor idle time and catch distracted students."
Help students and teachers understand digital learning rhythms so they can support attention, self-regulation, and better study habits.
Ten commitments we'd hold any school version to.
Designed around the spirit of FERPA and COPPA: education records and minors deserve the strictest defaults. These bind the research before any product exists.
We never capture the screen — not for students, not for anyone.
There is no code path that reads what a student types.
Never the contents of messages, documents, searches, or pages.
No focus grades, no behavior flags, nothing that feeds punishment.
If it runs, the student knows. Covert tracking is disqualifying.
The student sees their own patterns first — they are the primary audience.
Teachers and schools see class-level rhythms, not a per-student watchlist.
The purpose is self-regulation and better lesson design — full stop.
Students, parents, and schools can see what is and isn't collected.
We treat our hypotheses as hypotheses until the research earns the claim.
What a study would — and never would — collect.
Would collect — aggregated
- App / site categorieslearning, writing, research…
- Focus-block lengthshow long, not what
- Switch densityrhythm, aggregated
- Time-of-day patternswhen focus runs deepest
- Reflection responsesonly what a student writes
Never collect
- ✕Screenshots or screen contents
- ✕Keystrokes or anything typed
- ✕Message, document, or search content
- ✕Full URLs or page titles
- ✕Anything that produces a disciplinary score
- ✕Covert, student-invisible tracking
What we actually want to find out.
Stated as questions, not conclusions — because OS-level rhythm data for learning is a hypothesis, not an established fact.
The goal isn't "focus longer." It's the right rhythm.
Short bursts aren't automatically bad — spaced practice can help, while unstructured digital switching can hurt. The future is understanding which attention rhythm fits the task, age, and learning goal.
Shorter, structured focus periods with visible, planned breaks.
Self-monitoring with guided reflection on what pulled attention.
Longer independent focus blocks and a weekly attention review.
Deep-work protection and honest post-session reflection.
Built on primary research, not vibes.
The questions above sit on real evidence about multitasking, reflection, and learning analytics — and on the limits of that evidence. Learning-analytics research shows behavior data can support self-regulation, but rarely lifts achievement on its own without careful intervention design. We treat OS-level rhythm data as a hypothesis to test, not a proven lever.
- 1.May & Elder (2018) — Media multitasking & academic performance — May, K.E. & Elder, A.D. "Efficient, helpful, or distracting? A literature review of media multitasking in relation to academic performance." International Journal of Educational Technology in Higher Education, 15(13).
- 2.Sana, Weston & Cepeda (2013) — Laptop multitasking — Sana, F., Weston, T. & Cepeda, N.J. "Laptop multitasking hinders classroom learning for both users and nearby peers." Computers & Education, 62, 24–31.
- 3.Cepeda et al. (2006) — Distributed practice (spacing effect) — Cepeda, N.J., Pashler, H., Vul, E., Wixted, J.T. & Rohrer, D. "Distributed practice in verbal recall tasks: a review and quantitative synthesis." Psychological Bulletin, 132(3), 354–380.
- 4.Matcha et al. (2020) — Learning-analytics dashboards review — Matcha, W., Uzir, N.A., Gašević, D. & Pardo, A. "A systematic review of empirical studies on learning analytics dashboards: a self-regulated learning perspective." IEEE Transactions on Learning Technologies, 13(2), 226–245.
- 5.Li, Dey & Forlizzi (2010) — Stage-based model of personal informatics — Li, I., Dey, A. & Forlizzi, J. "A stage-based model of personal informatics systems." Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI '10), 557–566.
Want to shape this the right way?
Teachers, school leaders, and learning researchers: if a privacy-first attention-rhythm study sounds worth doing right, leave your email — or reach out directly. No commitment, no sales pitch.
Prefer to write? Contact us →