Indeed. “Privacy” makes it sound like a single factor, hiding the complexity of the matter and the importance of learners’ agency.

Useful breakdown. Research, predictive models, and recognition are quite distinct from one another and the approaches to data that they imply are quite different. In a way, the “personalized learning” model at the core of the second topic is close to the Big Data attitude (collect all the things and sense will come through eventually) with corresponding ethical problems. Through projects vary greatly, research has a much more solid base in both ethics and epistemology than the kind of Big Data approach used by technocentric outlets. The part about recognition, though, opens the most interesting door. Microcredentials and badges are a part of a broader picture. The data shared in those cases need not be so comprehensive and learners have a lot of agency in the matter. In fact, when then-Ashoka Charles Tsai interviewed Mozilla executive director Mark Surman about badges, the message was quite clear: badges are a way to rethink education as a learner-driven “create your own path” adventure. The contrast between the three models reveals a lot. From the abstract world of research, to the top-down models of Minority Report-style predictive educating, all the way to a form of heutagogy. Lots to chew on.

Stallman often uses a similar idea to condemn proprietary software. Rushkoff proposes a similar alternative. Should we choose the red pill or the blue pill?

Nice pun, giving another connotation to the term “learner”.

We share this goal. Maybe we focus too much on it. Maybe it’s just a new spin on an old idea. But it’s nice to have a group of pedagogues who want the same thing in this world.