Application of Data Mining Methods for Classification of Student Results from Conveying an ECTS Faculty Course
Date Issued
2011
Author(s)
Madevska Bogdanova, Ana
Abstract
In the large datasets (tables) containing grades gained by
students according to many criteria (attributes, i.e., columns
in the tables) for particular ECTS courses, it is a common
situation every criterion to have its own sub criteria, and these
sub criteria to be linearly combined to form the corresponding
criterion. Therefore, practically, for each student we get a
single record (single row in a table) with many attributes. The
main goal of our research was to assess how well selected
data mining methods are capable of detecting the linear
dependency of the final course grade from the course criteria.
To this purpose, we collected a dataset containing results
from a particular course held at our institution, and we made
appropriate experiments. We evaluated three different data
mining methods on this dataset (in its raw form) in order to
discover how well they would be able to model the criterion
for forming the final grade, and to estimate the classification
accuracy that they would achieve on independent test sets. In
this paper we study the performance of these data mining
methods on the dataset, analyze the results and point out
further directions for research.
students according to many criteria (attributes, i.e., columns
in the tables) for particular ECTS courses, it is a common
situation every criterion to have its own sub criteria, and these
sub criteria to be linearly combined to form the corresponding
criterion. Therefore, practically, for each student we get a
single record (single row in a table) with many attributes. The
main goal of our research was to assess how well selected
data mining methods are capable of detecting the linear
dependency of the final course grade from the course criteria.
To this purpose, we collected a dataset containing results
from a particular course held at our institution, and we made
appropriate experiments. We evaluated three different data
mining methods on this dataset (in its raw form) in order to
discover how well they would be able to model the criterion
for forming the final grade, and to estimate the classification
accuracy that they would achieve on independent test sets. In
this paper we study the performance of these data mining
methods on the dataset, analyze the results and point out
further directions for research.
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