An Impact of Intelligent Quotient and Learning Behavior of Students in Learning Environment

Data mining refers to mine the knowledge from large amounts of data. It is used to discover some new interesting patterns. Data mining techniques are used to find the association between the IQ performance and Learning behavior of the students. Cluster analysis is used to find the homogeneous data objects. It is used to decide the similarity of the students' data set based on the nature of the learning behavior. Each cluster reveals the identity based on its learning behavior of the student. The intelligence Quotient of the students is evaluated by the Stanford-Binet Intelligence Test and Criterion reference model. Multilayer Perceptron and EM clustering Technique is employed to classify the students based on the Intelligence Level. This experiment analysis could help the staff members to understand the student's behaviour and provide the suitable training for their improvement of academic competence. This paper reveals the intelligent quotient and the learning behaviour of the students in a learning environment.