Abstract—The Covid-19 pandemic has transformed the offline
education system into online. Therefore, in order to maximize the
learning process, teachers were forced to adapt by having
presentations that attract student's attention, including
kindergarten teachers. This is a major problem considering the
attention rate of children at early age is very diverse combined
with their limited communication skill. Thus, there is a need to
identify and classify student's learning interest through facial
expressions and gestures during the online session. Through this
research, student's learning interest were classified into several
classes, validated by the teacher. There are three classes:
Interested, Moderately Interested, and Not Interested. Trials to
get the classification of student's learning interest by teacher
validation, carried out by training and testing the cut area of the
center of the face (eyes, mouth, face) to get facial expression
recognition, supported by the gesture area as gesture recognition.
This research has scenarios of four cut areas and two cut areas
that were applied to the interest class that utilizes the weight of
transfer learning architectures such as VGG16, ResNet50, and
Xception. The results of the learning interest classification test
obtained a minimum validation percentage of 70%. The result
obtained through scenarios of three learning interest classes four
cut areas using VGG16 was 75%, while for two cut areas using
ResNet50 was 71%. These results proved that the methods of this
research can be used to determine the duration and theme of
online kindergarten classes.