Analysis of teacher-student learning style on student feedback using manhattan algorithm

This paper makes an attempt to implement learning style theory namely Visual, Auditory/Audio and Kinesthetic in technical institutes to make learning an enjoyable experience and student centric. The students learn in different ways and the teacher needs to design their course to meet these requirements of the students. The purpose of this investigation is to determine the learning style of the student and of the teacher and find the correlation between them. This will provide a way to approach students' needs and deliver the course content appropriately. Here learning styles of 44 students and the teacher who is teaching one of the courses is determined. The feedback was collected from students in order to compare the effectiveness of teaching in class. The experimental results indicate that when there is similarity between teacher and student learning style, the outcome is positive. Here VAK Learning Style (VAKLS) inventory developed by Victoria Chisslet is used. The Manhattan distance is measured to compare similarity in learning styles using Python 3.6 and feedback analysis is done with a standard formula. © 2018 IEEE.