Virtual learning and education have become crucial during the COVID19 pandemic, which has forced a rethink by teachers and educators into designing
online content and the indirect interaction with students. In an face-to-face class,
some visual cues help the teacher recognize the engagement level of students, while
the main weakness of the online approach is the lack of feedback that the teacher
has about the learning process of the students. In this paper, we introduce a novel
framework able ...
Virtual learning and education have become crucial during the COVID19 pandemic, which has forced a rethink by teachers and educators into designing
online content and the indirect interaction with students. In an face-to-face class,
some visual cues help the teacher recognize the engagement level of students, while
the main weakness of the online approach is the lack of feedback that the teacher
has about the learning process of the students. In this paper, we introduce a novel
framework able to track the learning states, or LS, of the students while they are
watching a piece of knowledge-based content. Specifically, we extract four learning states: Interested, Bored, Confused or Distracted. Finally, to demonstrate the
system’s capability, we collected a reduced database to analyze the affective state
of the subjects. From these preliminary results, we observe abrupt changes in the
LS of the audience when there are abrupt changes in the narrative of the video, indicating that well-structured and bounded information is strongly related with the
learning behaviour of the students.
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