Article with question mark in title? Answer likely “no.” But, it’s going to be some classrooms, for sure. An interesting use of computer vision and machine learning to generate metadata about student engagement. This could (should|will) be used for more than just lecturing, and what if students had access to their own data? This could be a powerful tool to support self-reflection on teaching and learning…
In a case of almost-perfect-timing, I presented to the iLab yesterday about some of my ideas for PhD work – looking for ways to help support self-reflection on teaching and learning. I talked about how student teachers use video of their teaching as a tool to learn teaching skills, and how video is basically an opaque blob of media that is extremely time-, labour- and cost-intensive to use for non-trivial projects. I then compared to some of the tools that enable some understanding of teaching and learning interactions in online learning, and that these types of tools are completely unavailable for face-to-face learning. These kinds of technologies can help, as long as they’re not weaponized into a surveillance culture or student assessment tools.
So, it’s already happening. We need to figure out what that means, and how to use this kind of thing for good before Pearson et al commercialize it and start selling silver bullets.
His team is looking into the use of facial recognition in classes, so lecturers will know when students start losing interest. “We want to see, when the lecturer delivers the lectures, whether the students are paying attention – do they grasp the idea or they show a doubtful face?”, he says. This will help lecturers intercede in their learning journey before exams, which would otherwise be “too late”, Tang says.
The analysis will be anonymised so that individual students’ identities are not revealed. “We do have to respect the students’ privacy”, he asserts. “That means we don’t go down to the individual student to say ‘this person was lost’, or ‘this person smiled’”. Machine learning will crunch out the analysis and give an aggregate review of class emotions. “This way, I feel that the personal privacy is being protected, yet still able to benefit the class and lecturer”.