TITLE:
A New Approach to Investigate Students’ Behavior by Using Cluster Analysis as an Unsupervised Methodology in the Field of Education
AUTHORS:
Onofrio Rosario Battaglia, Benedetto Di Paola, Claudio Fazio
KEYWORDS:
Education, Unsupervised Methods, Hierarchical Clustering, Not-Hierarchical Clustering, Quantitative Analysis
JOURNAL NAME:
Applied Mathematics,
Vol.7 No.15,
September
12,
2016
ABSTRACT: 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.