Abstract:
Concentration is the ability to focus the mind on a
specific context at a time. This is important to do any activities
efficiently such as learning and driving. Especially, for
students, it is tough to be stay focused because of the
distractions. A single moment of drifting in mind may cause a
significant impact on student’s performance. Therefore finding
that moment and alert the student to regain attention, would
help him to improve the ability to be concentrated while
learning. Several types of researches have been conducted to
find out the connection between concentration and human
related parameters such as heart rate variability, brain waves,
and facial expressions while learning. We propose a
methodology to combine these three parameters, expected to
overcome the limitations of one parameter by another. The
extracted features from each collected data from the relevant
sensors are fed into the classification models. As per the initial
experiments, the primary relationships were derived with
separate machine learning models for each parameter.
Citation:
N. Vettivel, N. Jeyaratnam, V. Ravindran, S. Sumathipala and S. Amarakecrthi, "System for Detecting Student Attention Pertaining and Alerting," 2018 3rd International Conference on Information Technology Research (ICITR), 2018, pp. 1-6, doi: 10.1109/ICITR.2018.8736145.