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Cameras in the classroom are usually trained on the teacher. Montgomery repeatedly emphasized that the technology, which SensorStar calls EngageSense, is in the research and development phase. That, coupled with audio, can be transformed into a rough, automated metric of student engagement throughout the day. After a lesson, a teacher could boot up EngageSense and see, with a glance at the dashboard, when students were paying rapt attention, and at what points they became confused or distracted.
I have a one year old daughter; it is distinctly possible that she does not need to learn how to drive when she grows up because self-driving vehicles will be the norm. As a computer scientist who works in a medical research institute, I witness firsthand how AI is transforming the way we screen our three-billion-character genome to discover disease-causing mutations, and detect cardiovascular risks by analysing data from wearable fitness trackers. The term AI may sound scary, possibly due to association with killer robots in science fiction.
As I look around at this freshman class, I have two students with the beginnings of mustaches and one with a full-grown neck-beard. Different schools apparently have different rules. So far so good.
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Research from North Carolina State University shows that software which tracks facial expressions can accurately assess the emotions of students engaged in interactive online learning and predict the effectiveness of online tutoring sessions. This is important because research shows that student emotion plays an important role in the learning process. The researchers used the automated Computer Expression Recognition Toolbox CERT program to evaluate facial expressions of 65 college students engaged in one-on-one online tutoring sessions.