Deriving Association between Learning Behavior and Programming Skills

Abstract

Data mining is the process of discovering patterns from large amount of data stored in student database. It predicts the facts from the dataset and interpreted as useful information to the society. The learning behavior of the student and programming skill are dissimilar in a classroom and programming lab. Data mining techniques are used to find association between the Learning behavior and programming skills using students’ dataset. Clustering is used to determine the similarity in the students’ dataset based on the nature of the learning behavior and programming skills. Each cluster reveals the identity based on its learning behavior of the student. Likewise the programming skill is categorized based on descriptive modeling. Multilayer Perceptron (MLP) technique classifies the learning behavior of students and their programming skills based on learning by example. The task is to determine the association between learning behavior and programming skills through descriptive and predictive modeling using mapping or function. It reveals that there is a positive correlation between student learning behavior and programming skills. This analysis could help the staff members to provide right training to the students for their improvement of programming skill.