An Assessment for Understanding Student Behaviour by Applying Machine Learning Technique

Abstract

In the past, research and tests have been carried out to study the behaviour of students as it is an important topic in psychology. Parents and teachers are concerned about their children how to act in class. Learning of students behaviour in school is essential for teachers for the development and growth of them. A class is composed of students with distinctive characteristics and capacities where a few students are sharp and some are dull. In some cases, it becomes troublesome for the educators to spot who is picking the pace with them and who is falling behind. The proposed approach will extend the students to groom their personalities and overcome their shortcomings and it will offer assistance to the instructor to identify which students require more consideration from them. To do that, we chose and applied K-Clustering Algorithm. In other words, this research attempt to discover homogeneous subgroups inside the information. K means calculation is an iterative calculation that tries to parcel the dataset into K pre-defined unmistakable non-overlapping subgroups (cluster) where each information point has a place as it were one bunch. It tries to form the intra-cluster information focuses as comparable as conceivable whereas moreover keeping the cluster as diverse (distant) as conceivable