3 Matching Annotations
  1. Jul 2018
    1. the period for which the personal data will beretained in terms of section 10 or where such period is not known, the criteria for determining such period;

      This defines the terms for data retention. From a company perspective, they are likely to keep this as broad as possible.

  2. Jun 2018
    1. (Roose, who has since deleted his tweet as part of a routine purge of tweets older than 30 days, told me it was intended simply as an observation, not a full analysis of the trends.)

      Another example of someone regularly deleting their tweets at regular intervals. I've seem a few examples of this in academia.

  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?