Anytime you hear someone say “personalization” or “AI” or “algorithmic,” I urge you to replace that phrase with “prediction.”
đź“‘ Machine Teaching, Machine Learning, and the History of the Future of Public Education
I think there’s a lot to say about machine learning and the push for “personalization” in education. And the historian in me cannot help but add that folks have trying to “personalize” education using machines for about a century now. The folks building these machines have, for a very long time, believed that collecting the student data generated while using the machines will help them improve their “programmed instruction” – this decades before Mark Zuckerberg was born.
I think we can talk about the labor issues – how this continues to shift expertise and decision making in the classroom, for starters, but also how students’ data and students’ work is being utilized for commercial purposes. I think we can talk about privacy and security issues – how sloppily we know that these companies, and unfortunately our schools as well, handle student and teacher information.
But I’ll pick two reasons that we should be much more critical about education technologies.
In some prepared remarks, delivered on a panel titled “Outsourcing the Classroom to Ed Tech & Machine-learning: Why Parents & Teachers Should Resist” at the Network for Public Education conference in Indianapolis, Audrey Watters highlights two major points of concern. Firstly, (ed)tech is a black box providing little understanding of how to works. While secondly, AI is biased. Machine learning is better considered as machine predictions.