Sort student attention for the creation of individualised learning systems.
Abstract
Researchers have made several attempts to categorise student attention in investigations. Numerous of these methods relied solely on qualitative analysis and did not include any quantitative analysis. Therefore, the goal of this work is to close the classification of student attention gap between qualitative and quantitative techniques. Therefore, utilising information from a consumer RGB-D sensor, this research employs machine learning methods (K-means and SVM) to automatically identify pupils as attentive or inattentive. The outcomes of this study can help instructors adopt individualised learning systems, which is a National Academy of Engineering Grand Challenge, and can also be utilised to enhance teaching tactics for instructors at all levels. In this study, machine learning techniques are used in a learning environment.
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