In web-based educational
environments predict student’s performances is very important where the
students who are at the risk of failing examinations can be identified at the
early stage of the course modules and the educators can take necessary actions
to improve their knowledge to a more higher level and to increase their
learning capacities as well.
In data mining context use of
classification on the educational data is a upcoming research area where to
discover potential student groups with similar characteristics and to identify
learners with low motivation and find corrective actions to lower drop-out
rates.
C. Romero, S. Ventura, P. G.
Espejo, and C. Hervás [2008] have tried to used different classification
approaches on the student data to
compare the applicability on data mining techniques for classifying the
students in to groups and to predict the final marks obtained in the course
modules.
In their research they used a
framework which is known as KEEL which is an open source framework for building
data mining models including classification, regression, clustering, pattern
mining and based on this framework they developed an data mining tool which can
be integrated in to the moodle environment.
