Abstract
In day to day classroom student behaviors such as troublesome, awful avoidance of work, interfering with teaching activities, harassing classmates, rudeness to teachers, daydreaming in class can vary from mild to severe is a problematic issue. These issues are reported by the teacher as intolerable and stress provoking. Every teacher must spend a lot of time to manage these kinds of students. These kinds of misbehaviors can affect the effectiveness of teaching and it may also affect the proper learning of the student. We can conclude that all the above-mentioned issues are comes under inattentiveness of a student. This work is focused on bridging the gap between qualitative and quantitative approaches to classify student inattentiveness. Thus, this research applies K-means and ANN machine learning algorithms to automatically identify and classify the inattentive students by using Kinect RGB-D sensor. Results of this research can be used to recruit teachers with years of teaching experience and training background to get an apt solution for the issue.
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