Educational technology in an age of Trump: a risk to students?

I find this suggestion by Audrey Watters extremely plausible. Full interview here.

I think that education data should be a top priority under the new Trump regime. Schools are wildly obsessed with collecting data. They have been for a very long time, but new digital technologies have compelled them to collect even more, all with the promise of better insights into teaching and learning. By and large, I think a lot of that promise is overstated. Now, particularly under Trump, we have to consider if, instead of “helping students”, we’re actually putting them more at risk. I don’t simply mean a risk of hacking, although schools do have notoriously poor information security. Rather, I’m deeply concerned that, by enabling such expansive profiling, we are furthering a dangerous climate of surveillance – a climate that Trump seems quite ready to exploit regarding undocumented immigrants, Muslims, and political dissidents.


  1. Yes, it is plausible. However, it is “complicated” in America. A lot of people outside of America (including Canada) don’t realize how American education works – or rather doesn’t work. It is highly racialized, and will become more racialized with the new legislation before congress with the new education secretary.

    First is that Public primary education USED to produce a lot of Ph.D.s and Noble winners. Then came the 1980s. Today, most students in public schools are in urban ghettos made up of primarily African-Americans, and are highly segregated. There are several school boards that are still (in 2017) under federal court order to desegregate from the 1954 Brown v Board Supreme Court decision.

    Today, most school teachers are required to have Masters degrees. Most urban public school teachers, with their Masters degrees earn about $20,000 per year or less. Many are on social assistance. Many public school boards struggle just to buy textbooks because of funding cuts since the 1980s. Some are using textbooks that are decades old. Those school certainly do not have computers; and if they do, they are old, and not securable.

    The main complaint about public education in America is that they aren’t producing educated children (as measured with standardized testing). What is almost NEVER talked about is that they used to produce Ph.D.s and Nobel winners – until teachers had to go on welfare and not have textbooks.

    Any changes to public education are much more likely to impact African-American students. Currently, with the advent of Trump, there is legislation to eliminate the U.S. Department of Education entirely, and completely de-fund public schools – giving parents “vouchers” instead, to enroll their children in the private school of their choice. Which for urban African Americans, there are nearly none.

    So while plausible, if public schools cannot afford textbooks, are segregated, put teachers on welfare, and are about to be shut down, what’s the point?

      1. In relation to what Watters is arguing is two-fold: 1) if schools cannot invest in textbooks, how are they going to invest in data-gathering, and 2) it assumes that political institutions are actually concerned with facts, when the facts have been clear about how schools are operating (and why), yet continue to starve schools out of existence.

        Should there be a concern for data-gathering? Sure. There should also be a concern that most public schools do not have textbooks. There should also be a concern that most public school teachers are so underpaid that they have to receive social assistance. Any data gathered to gain insights of teaching and learning will automatically be flawed in this backdrop. You cannot gain accurate insights into teaching and learning models when both kids and teachers are physically hungry, while teaching and learning with no (or at least substandard) materials. Do we want data, or do we want “accurate” data?

  2. I think you are correct in your assessment of me. Is that a bad thing? In my economic master’s program I got a “B” on a paper because I didn’t perform enough “mathematical gymnastics” in a regression to get the results to reject the null. The teacher admitted that there was nothing wrong with my model, and the results were solid based on the model. Instead, I worked with the results I had, and not the results that were “expected.” This is a methodological issue that I stood my ground on; I would not engage in what I call “Machiavellian data manipulation” – where the ends justify the means. I ended up with an “A” in the course, no no harm, no foul for me. However, this methodological thinking is symptomatic of data gathering today. From p-hacking to over-weighting variables, we often create the social facts that we want instead of analyzing social facts as they are.

    We know as a social and medical fact that child’s learning outcome is lower when they’re physically hungry as a stand-alone variable all by itself. If we look at student-teacher outcomes without considering that antagonizing variable of hunger, then all we’re really doing is a “Machiavellian data manipulation” to create the social facts that we want, instead of seeing the social facts as they actually are.

    My apologies for my “thickness!” But thank you for reminding me of that! 🙂

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