Author(s)

Onofrio Rosario Battaglia¹, Benedetto Di Paola², Claudio Fazio¹

¹University of Palermo Physics Education Research Group (UOP-PERG), Dipartimento di Fisica e Chimica, Università di Palermo, Palermo, Italia.
²Mathematics Education Research Group (GRIM), Dipartimento di Matematica e Informatica, Università di Palermo, Palermo, Italia.

The problem of taking a set of data and separating it into subgroups where the elements of each subgroup are more similar to each other than they are to elements not in the subgroup has been extensively studied through the statistical method of cluster analysis. In this paper we want to discuss the application of this method to the field of education: particularly, we want to present the use of cluster analysis to separate students into groups that can be recognized and characterized by common traits in their answers to a questionnaire, without any prior knowledge of what form those groups would take (unsupervised classification). We start from a detailed study of the data processing needed by cluster analysis. Then two methods commonly used in cluster analysis are before described only from a theoretical point a view and after in the Section 4 through an example of application to data coming from an open-ended questionnaire administered to a sample of university students. In particular we describe and criticize the variables and parameters used to show the results of the cluster analysis methods.

Education, Unsupervised Methods, Hierarchical Clustering, Not-Hierarchical Clustering, Quantitative Analysis

Battaglia, O. , Paola, B. and Fazio, C. (2016) A New Approach to Investigate Students’ Behavior by Using Cluster Analysis as an Unsupervised Methodology in the Field of Education. Applied Mathematics, 7, 1649-1673. doi: 10.4236/am.2016.715142.