  The commencement of the decade brought along with it a grave pandemic and in
response the movement of education forums predominantly into the online world.
With a surge in the usage of online video conferencing platforms and tools to
better gauge student understanding, there needs to be a mechanism to assess
whether instructors can grasp the extent to which students understand the
subject and their response to the educational stimuli. The current systems
consider only a single cue with a lack of focus in the educational domain.
Thus, there is a necessity for the measurement of an all-encompassing holistic
overview of the students' reaction to the subject matter. This paper highlights
the need for a multimodal approach to affect recognition and its deployment in
the online classroom while considering four cues, posture and gesture, facial,
eye tracking and verbal recognition. It compares the various machine learning
models available for each cue and provides the most suitable approach given the
available dataset and parameters of classroom footage. A multimodal approach
derived from weighted majority voting is proposed by combining the most fitting
models from this analysis of individual cues based on accuracy, ease of
procuring data corpus, sensitivity and any major drawbacks.
