Teaching across rural classrooms, I kept hitting the same wall: I could see exactly what a student did, which question, which wrong answer, how long they took, and still had no way to see why. Was it forgetting, a misconception, or a missing prerequisite? Every system I had access to recorded the output and threw the reason away.
That gap is the whole problem. A score marks an answer right or wrong; it never reads the process that produced it. The cognitive science needed to read that process, how memory decays, what a retrieval attempt reveals, how one kind of error differs from another, has existed for a century. It had simply never been turned into a live measurement anyone could act on.
So I built Cognivia, an instrument that reads what a learner is actually doing while they answer, works out why an answer failed, and hands back what to try next. It runs in a browser on one shared device, with nothing to install, because the classrooms I had in mind cannot count on more than that, whether the learner is in a village school here, a library in Lagos, or a bedroom in São Paulo.
I locked the study design before collecting any data, and I report the analysis as it comes, including the parts that go against what I predicted. If the main result comes back null, it stays on the site, and I fall back to the diagnostic on its own, which is still useful whether or not the scheduling idea holds up.
I started it because a question would not leave me alone, and I built it where that question is hardest to answer. If it works, there is plenty to build on top of it, but the science has to come first, or the rest is not worth trusting.
One plain note, so there is no confusion. One person built this, alone, over a gap year in Kanpur, and you can see it in the rough corners if you open the source. It has nothing to do with any other product or platform that happens to share the name. The code and the data are public, so you do not have to take my word for any of it.
Manik Maurya
In brief
I locked the analysis plan before the first student logged in, and I report whatever comes back.
The world teaches roughly 1.5 billion students in school every year. A tiny fraction of them are reached by anything resembling adaptive instruction. The classroom is not going to be rebuilt around each individual learner, not in any country, not in any decade I will be alive for. What can be rebuilt is the few minutes of revision each of those learners does on their own.
So this study asks a narrow question: does adapting the schedule to the type of error a learner makes improve retention? If the answer is yes, it is portable. It runs on a phone or a shared classroom computer, and needs no teacher to install it and no school to approve it. It is, in the most literal sense, the only intervention I know how to build alone, from Kanpur, that could matter to a learner I will never meet.
This trial tests one claim that can be proven wrong: that error-type-aware remediation beats adapting on correctness alone. I report the result either way, with the data and protocol public. A null would mean that specific claim was wrong, not that the work ends, because the Genome and the Passport, the measurement the scheduling is built on, are useful as a diagnostic on their own, whatever the scheduling result.