• Krunaal Tavkar
• Vivek Gosavi
• Akshay Kumar
• Roshan Ingawale
• Gitanjali Mate

In a world that is constantly changing, our education systems have more or less stayed the same. The adoption of smartboards and smart-classroom setups have been the only innovation in teaching methodology. The chalk – and – talk style of teaching, is still the most widely used method in Indian Education. Although teaching methodologies have been a part of the growing innovation, there is no way to measure or identify the emotions of a student during a lecture. The entire purpose of a lecture is to impart knowledge to the students and ensure maximum retention of the concept being taught. But, if a student is bored during or half-way through a lecture, it is very rare that he/she has been able to retain more than 50% of the contents taught during that lecture. In this paper, we attempt to discuss potential solutions, based on Machine Learning, that can help track and identify student emotions during a classroom lecture. They have been derived from various methodologies and ideas that have been presented in the literature and portray a view of the current state-of-the-art research in this exciting field.

Machine Learning, Boredom, Identify Emotions, Smart-Classroom, Maximum Retention.

"Boredom Level Detection Using Machine Learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.5, Issue 5, page no.267-270, May-2018, Available :http://www.jetir.org/papers/JETIR1805644.pdf

"Boredom Level Detection Using Machine Learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.5, Issue 5, page no. pp267-270, May-2018, Available at : http://www.jetir.org/papers/JETIR1805644.pdf