Classroom engagement evaluation using computer vision techniques

Classroom engagement is one important factor that determines whether students learn from lectures. Most of the traditional classroom assessment tools are based on summary judgements of students in the form of student surveys filled out in class or online once a semester. Such ratings are often bias and do not capture the real time teaching of professors. In addition, they fail for the most part to capture the locus of teaching and learning difficulties. They cannot differentiate whether ongoing poor performance of students is a function of the instructor's lack of teaching skill or the student's lack of engagement in the class. So, in order to streamline and improve the evaluation of classroom engagement, we introduce human gestures as additional tool to improve teaching evaluation along with other techniques. In this paper we report the results of using a novel technique that uses a semi-automatic computer vision based approach to obtain accurate prediction of classroom engagement in classes where students do not have digital devices like laptops and, cellphones during lectures. We conducted our experiment in various class room sizes at different times of the day. We computed the engagement through a semi- automatic process (using Azure, and manual observation). We combined our initial computational algorithms with human judgment to build confidence the validity of the results. Application of the technique in the presence of distractors like laptops and cellphones is also discussed.