Multimodal Classification of Teaching Activities from University Lecture
  Recordings
Oscar Sapena Eva Onaindia
The way of understanding online higher education has greatly changed due to
the worldwide pandemic situation. Teaching is undertaken remotely, and the
faculty incorporate lecture audio recordings as part of the teaching material.
This new online teaching-learning setting has largely impacted university
classes. While online teaching technology that enriches virtual classrooms has
been abundant over the past two years, the same has not occurred in supporting
students during online learning. {To overcome this limitation, our aim is to
work toward enabling students to easily access the piece of the lesson
recording in which the teacher explains a theoretical concept, solves an
exercise, or comments on organizational issues of the course. To that end, we
present a multimodal classification algorithm that identifies the type of
activity that is being carried out at any time of the lesson by using a
transformer-based language model that exploits features from the audio file and
from the automated lecture transcription. The experimental results will show
that some academic activities are more easily identifiable with the audio
signal while resorting to the text transcription is needed to identify others.
All in all, our contribution aims to recognize the academic activities of a
teacher during a lesson.