Abstract:
In recent years, student engagement estimation
has gained focus in the affective computing community. The
absence of student monitoring during online MOOC courses
makes it challenging to estimate behavioural student engagement during online classes. The non availability of consistent
engagement datasets makes it difficult to build cross data
automatic behavioural engagement estimation technique. In this
paper, we propose an unsupervised topic modeling technique
for engagement detection as it captures multiple behavioral
cues which are indicators of engagement level such as eye gaze,
head movement, facial expression and body posture. We have
addressed the various challenges such as less volume of our
datasets, large decision unit (annotated for 5 minutes duration)
and uneven distribution of different engagement categories
with domain adaptation based solution for cross data implementation. We present results on engagement prediction using
different clustering techniques such as K-Means and Latent
Dirichlet Allocation (LDA) along with different regressors and
neural network based attention mechanisms.
