predictive algorithms to better target students’ individual learning needs.
Personalized learning is a lofty aim, however you define it. To truly meet each student where they are, we would have to know their most intimate details, or discover it through their interactions with our digital tools. We would need to track their moods and preferences, their fears and beliefs…perhaps even their memories.
There’s something unsettling about capturing users’ most intimate details. Any prediction model based off historical records risks typecasting the very people it is intended to serve. Even if models can overcome the threat of discrimination, there is still an ethical question to confront – just how much are we entitled to know about students?
We can accept that tutoring algorithms, for all their processing power, are inherently limited in what they can account for. This means steering clear of mythical representations of what such algorithms can achieve. It may even mean giving up on personalization altogether. The alternative is to pack our algorithms to suffocation at the expense of users’ privacy. This approach does not end well.
There is only one way to resolve this trade-off: loop in the educators.
Algorithms and data must exist to serve educators
more on algorithms in this IMS blog