Interaction between lecturers and students plays a critical role in defining one's view of a lecture. However, with an increasing number of students enrolled in universities every year and limited classroom space available, classrooms are often overcrowded. As a result, it is fairly difficult for lecturers to immediately observe the learning feedback from all students on the lecture being delivered. In this paper, we propose a tool named Facial Expression Analysis Tool (FEAT) to help lecturers in universities in evaluating the effectiveness of their lecture by evaluating their students’ facial expression based on three facial expressions: bored, satisfied or confused. The tool utilizes dual CNN for detection and classification. FEAT receives the video feed via an IP camera from the classroom, and analyzes and stores the information on a cloud database. The aggregated information from the database is further filtered, and the statistical details are displayed on a visual dashboard on the web. The tool was evaluated in a real classroom environment and found to have achieved a good accuracy. The tool provides useful insights for the lecturers to better observe their students’ perception on their lectures and improve their teaching approach if required.