Ethics in the collection, analysis and use of student data
Moodle core now implements open source, transparent next-generation learning analytics using machine learning backends that go beyond simple descriptive analytics to provide predictions of learner success, and ultimately diagnosis and prescriptions (advisements) to learners and teachers.
In Moodle 3.4, this system ships with two built-in models:
- Students at risk of dropping out
- No teaching activity
While we can do our best to inform students, the black box nature of the web means that we can never definitively say to them: "This is what you are going to be a part of." The fact that the web functions the way it does is illustrative of the tremendously powerful economic forces that structure it. Technology platforms (e.g., Facebook and Twitter) and education technologies (e.g., the learning management system) exist to capture and monetize data. Using higher education to "save the web" means leveraging the classroom to make visible the effects of surveillance capitalism. It means more clearly defining and empowering the notion of consent. Most of all, it means envisioning, with students, new ways to exist online. -- Chris Gillard, "Pedagogy and the Logic of Platforms"
Ethics in the collection, analysis and use of student data
By Brian Lamb
