Using Data Mining on Students’ Learning Features: A Clustering Approach for Student Classification

Abstract

Students have different levels of motivation, approaches to learning, and intellectual levels. The better that instructors understand these differences, the better the chances they have of improving their quality of teaching. To explore differences thoroughly, we focuses on three crucial factors in student learning features – i.e., personality, learning style and multiple intelligences – and propose an approach effective in classifying students for the purpose of instructing instructors while optimizing their teaching process. We collected data on learning features from a class of 58 college students and analyzed these data by using principal component analysis (PCA) and then classified them using Ward clustering. Results of experiments indicate that our proposal effectively classifies students based on their learning features and that classification results facilitate instructors in creating personalized teaching strategies.