Thanks for the respectful and nuanced take.

I am somewhat concerned with where this stuff leads, not because I assume it's a one step process between education analytics and oppressive dystopia, but precisely because the use cases you describe seem like great ideas.

I agree that minimizing drop-out rates is important, and that we should avail ourselves of whatever tools we can to do it. What you're describing does not bother me, and seems like a reasonable use of the data.

So why not take it a step further? Let's authorize a limited release of student-identifying data to government social work agencies (like the DHS in the United States), so that concerning student performance data could be correlated with whether a student's family was on income/food/housing assistance. Heck, to maximize the signal, we could even correlate it with families who aren't on assistance but whose homes are likely to be foreclosed upon (leading to a likely student move, possibly changing schools--a very disruptive educational experience whose impact can be mitigated with careful preparation). Why not correlate with criminal records, to help promote after-school programs to students who would otherwise be returning to troubled or dangerous homes?

To be clear, I'm bothered because those things all seem like good ideas. I think they'd probably work, if done properly. And would probably help a lot of people. The systems we have currently to try to accomplish those things (overworked social workers, underfunded public-education and after-school programs) aren't succeeding.

...they'd also result in a de facto surveillance state.

If that data were compromised by or sold to an outside (nongovernment) entity, the damage due to the breach would be very severe, from inability to get financing (because lenders know details about your life that may not be reflected on your credit score) to individual persecution (e.g. employer discrimination against family members whose leaked data indicated their financial/housing situation may put their long-term employment at risk).

If the government programs using that data were "compromised" (i.e. staffed, perhaps via an electoral sea-change, with discriminatory people), the damage would be even worse. School funding allocation could suffer because someone at the top decided "Our data shows that so many of the students at $school_x have so much else going against them in their lives--why bother? Starve that school for funding and let it wither; that money would be better allocated to schools attended by our kids/kids of different races/kids with higher income." Additionally, correlations could be dishonestly interpreted by bigoted officials ("the algorithm says that female/male/dyslexic/$race_x kids are a lost cause!").

There are other, logistical issues. If school staff (i.e. counselors) have direct access to all this information, could they discriminate against kids and their families if bigoted? If school staff didn't have direct access, will the right conclusions be drawn by distant analysts (or worse, algorithms) without the personal context of counselors and teachers? A failure to consider all those factors could result in IEP-like things that harm, rather than help (consider: "You talked back to counselor Bob, so he won't authorize you for additional meal points or recommend you for after-school tutoring in an area where you're struggling", or "the DOE algorithm decided you need to take more English classes even though counselor John has been working on getting you individual reading time because he knows there's a bullying risk from other kids in the English program").

Many of these things are already happening, in varying degrees. I worry that making ubiquitous data available could make them worse, if it is not done carefully.

I am also sensitive to the benefits that education analytics (and more pervasive, cross-cutting data availability for government use across the board) can provide. I think the answer is to proceed carefully (i.e. the opt-out buttons you discuss, and careful data anonymization whenever it makes sense to do so) and to insist on comprehensive regulation regarding data exchange between government programs, and between those programs and non-government entities.

EDITS: explained logistical issues. Typos.