I was reading those e-Literate guys, Michael Feldstein and Phil Hill, write about personalized learning at edSurge the other day. Oddly enough, I was writing my own little account of the same phenomenon for the book I’ve been working on at the same time. While what follows obviously owes a lot to Audrey Watters, I still thought I’d drop in the first draft of this section of Education Is Not an App for the sake of diversity of opinion and so that this blog doesn’t go completely dead before the book comes out. [I’ve hyperlinked the material that originally came from the web and omitted footnotes.]
ELIZA was a computer program written by Joseph Weizenbaum of MIT between 1964 and 1966 that simulated a psychotherapy session. Type a question into the program, and ELIZA would create another question based upon the language in the original question. For example, if you wrote something along the lines of “My mother is making me angry,” the computer might respond with a question like “Tell me more about your mother.” Despite ELIZA’s simplicity, people using it tended to quickly get entranced by the opportunity to talk about themselves. Weizenbaum himself was deeply concerned that people were being fooled into thinking that the machine actually cared about them.
Computers have gotten a lot more sophisticated since the 1960s. As a result, the kinds of conversations that they can carry out have gotten more sophisticated too. “The holy grail of learning is personalized or adaptive learning,” explained Anant Agarwal of edX in April 2015. “This form of learning is what you might experience from an excellent personal tutor who is able to tailor your individual experience. In many ways, adaptive learning can be compared with those old ‘Choose Your Own Adventure’ books. At each step in the learning process, the user is given multiple options that satisfy his or her level of comprehension, style or direction. They may all lead to the same place (mastery of the material). but the path can be very different and structured for a particular learner.” While Agarwal mentioned this in the context of edX’s MOOCs, there’s no reason that this technology has to be scaled to thousands of people at once. It would, at least in theory, work just as well with twenty students as it would with twenty thousand.
Of course, advocates of this kind of technology in the classroom will tell you that it is not intended to replace teachers. In an Inside Higher Education article about work being done at the University of Wisconsin – Madison on what researchers there have dubbed “machine teaching,” one of the principle investigators told the reporter “this will not minimize the teacher or faculty member role, but would help to optimize the teacher’s time, so he or she could spend the least amount of time necessary on a subject before every student fully understood it.” Unfortunately, the professors who develop this technology have no power over exactly how it gets employed – especially if it ever gets licensed by private companies. Arguing that automation will free up workers to concentrate more meaningful work is a common argument in Silicon Valley. As the author Nicholas Carr explains, “high-flown rhetoric about using technology to liberate workers of masks contempt for labor.”
Look at this situation from as administrator’s point of view. If they buy these expensive computer programs, where will they get the money to pay for them? At cash-strapped schools the inevitable justification will be because it can save labor costs. Computerized teachers, computerized scoring – these days computers will even tell students whether they’re on the most efficient path towards graduation, thereby eliminating the need for advisors. Sometimes it seems as if every aspect of modern universities that can be mechanized has been mechanized. Why would actual teaching be any different?
Making this kind of switch depends upon advocates of technology changing the definition of what education is. The classes where we attended college depended upon prolonged interaction between the instructor and the students. Even in the largest classes that we took, professors took questions before, during and after their lectures. If we were feeling shy, there was ample opportunity to visit professors or our teaching assistants in office hours to work through whatever problems we had in the material. Our papers were graded by human beings who explained why we earned the grades we did. It is through these kinds of exchanges that the most intense learning happens, the ones where habits of mind are set and where inclinations develop into skills that students can employ not just in other classes, but for the rest of their lives (long after they’ve forgotten what their undergraduate professors’ names happened to be).
Personalized learning, the pitch goes, allows professors to spend less time doing things that others can do better (like lecture), you can spend more time helping students learn. Unfortunately, like Lucy and Ethel in that chocolate factory back in the 1950s, it is easy for your employers to speed up your line by giving you more students – particularly if you work in an online setting where the size of the classroom is no longer a limiting factor. In this way, unbundling the professor’s job limits the contact between the student and their instructor. By limiting the contact between students and their instructor, unbundled classes have to focus on how much content a student remembers rather than the kinds of skills that they develop since testing for those skills requires more labor, not less. Anyone who really cares about teaching should consider that outcome a shame.