4 Matching Annotations
  1. Mar 2024
  2. Oct 2021
  3. Nov 2017
    1. Mount St. Mary’s use of predictive analytics to encourage at-risk students to drop out to elevate the retention rate reveals how analytics can be abused without student knowledge and consent

      Wow. Not that we need such an extreme case to shed light on the perverse incentives at stake in Learning Analytics, but this surely made readers react. On the other hand, there’s a lot more to be said about retention policies. People often act as though they were essential to learning. Retention is important to the institution but are we treating drop-outs as escapees? One learner in my class (whose major is criminology) was describing the similarities between schools and prisons. It can be hard to dissipate this notion when leaving an institution is perceived as a big failure of that institution. (Plus, Learning Analytics can really feel like the Panopticon.) Some comments about drop-outs make it sound like they got no learning done. Meanwhile, some entrepreneurs are encouraging students to leave institutions or to not enroll in the first place. Going back to that important question by @sarahfr: why do people go to university?