Evaluation and Assessment of Teaching Quality and Students’ Performance using Machine Learning

Abstract

In quality education, we except good teaching. To improve the academic performance of a student it is important to monitor the students’ activities and attentiveness. The traditional way to do that is Examination and regular counselling; But an exam can’t conclude overall academic performance of a student, counselling is not enough to estimate the difficulties that students are facing in classroom, the level of attention of students in classroom and the favorite subjects of the students. And it’s also important to monitor the teachers’ teaching quality. In this work we proposed an AI model, by which we can analysis the students’ emotion, activities and attentiveness in the classroom with the help of high-resolution configured camera in the classroom. The model detects the facial expressions of each student in classroom to recognize the emotion of each student; and detect the eyes, lips and head movement of each student to recognize the activities of the students. And analyze these recognized activities for each student to classify the attentiveness status (attentive or inattentive) of each student. And an overall activities and emotion of all the students will be analyzed to evaluate the teacher’s teaching quality at the class – If most of the students are inattentive or not happy, means the teacher’s teaching methods is not helping the students and vice versa. The proposed machine leaning model not only analyze performance of the student and teaching method, it also alerts the teachers about the unwanted activities of the student in the classroom in real-time.