This is putting the tool before the horse, if you'll let me mix two metaphors Michael uses. Could the academic ecosystem be renewed in such a way that the existing product category we are trying to justify here was unnecessary?

Yes, let's imagine (and actually work toward) this, rather than new kinds of vendor relationships.

Perhaps students could better learn skills from another human.

In the back of my mind is how some students at one community college I know that deployed Starfish called it "Star Snitch" because they always felt it was ratting them out, not helping them.

Predicting is a product category, but preventing is not (unless you count the EDUs themselves, which is probably where the responsibility for prevention should be located).

Again, the commerciality of their social deployment would make it more difficult for them to tailor for success OR address ethical concerns.

This is where the social deployment of tech/practices can start to take over. First, commercial interests are now in charge of surfacing the ethical dilemma, which may not be in their best commercial interests to prioritize. Second, their customers have outsourced concerns and so are less interested/committed/resourced to address them.

Seems that this first challenge exists wether humans or machines are watching/intervening, even if machines might provide different options (eg, being able to watch private data and surface it without revealing it).

I'm not sure what a "Facebook" problem is. It seems to me that many of the same ethical questions arise in the social media realm — or at least related questions, equally important.

Indeed. And it should be of no surprise because the social deployment of a technology/practice pretty much always leads to ethical considerations.

When data exists, it is also often seen by unauthorized people.

This is a warning sign to me. Do we really want to use credit scores as a model for ANY activity in education?

This is key: the machines aren't really doing anything better than humans, but they do make it easier to scale. Are there studies that compare student success for larger classes assisted by machines with smaller classes with smaller teacher to student ratios? Would human participation and intervention be better than machines? Would it be cheaper or more expensive? What ofter effects, desirable or not, would the machine and/or human models have?

From the start, the educational and commercial fate of predictive analytics are intertwined.

For me, a first question about how such information would actually be used is whether it might not be used just as often to deter students from entering specific classrooms (or even institutions) as used to help them succeed once there.

Behind any technology/practice being deployed today in EDU is the larger circumstances of trying to scale/optimize, and often, privatize, education. There are strong winds blowing stuff that could be used to help in other directions.

How is Boolean converted to Number ??

https://www.youtube.com/watch?v=oqB-sz2SBsg

And now underway? In paper.

Highlighted this in response to Jeremy's comment above about filters. Institutional practices and policies are the focus here as well as the physical infrastructure for IT, available equipment.

As a "virtual attendee" or "lurker" or "scoper," it was difficult to follow and frustrating at times, but super helpful and fun too, especially as resources (images, links, quotes) were shared out. Communicating the context of those resources is still a pretty tricky thing to do well.