Lifestream, Diigo: Predictions, Probabilities, and Placebos | Confessions of a Community College Dean

Concerns about predictive analysis – does it introduce ‘stereotype threat’, in which learning that “people like me aren’t good at x” has an affective impact on performance? Steele, quoted in the article, suggests that awareness of negative stereotypes diverts cognitive resources. In this sense, the author (Matt Reed) contends that predictive analytics have the potential to recreate existing economic gaps.

I would say it works from the other side too: teachers who know a student has a bad behavioural or ‘performance’ record often treat them differently, as though they are already a problem.

Reed proposes that we may have ‘a positive duty to withhold data that would do active harm’. Sounds fair on the one hand – but given the option of conducting a ‘statistical placebo’ I feel uncomfortable. We don’t all respond to information in the same manner; perhaps for some students the negative predictions would be valuable. Should students have a right to the predictions?

In a follow-up article Inside Digital Learning asked the leaders of companies from predicitive analytics for a response. Key points/quotes included:

  • It’s not about what the information is, it’s about how you deliver it (i.e. support [which, as Enyon, 2013, p. 238 notes, has financial implications for providers], talking about a student’s options);
  • The type of data you share matters: “It’s not a matter of whether you should share predictive data with students or not, it’s a matter of sharing data they can act on,” Dave Jarrat from Inside Track (i.e. being told that you’re likely to fail/discontinue your studies isn’t useful – you need to know what students who were in your position and succeeded did);
  • Individual responses to data need to be taken into consideration.

Irrespective of whether the responses led me to a personal stance, they speak very loudly of the learnification of education.

from Diigo