Clustering Students According to their Academic Achievement Using Fuzzy Logic
CoRR(2023)
摘要
The software for clustering students according to their educational
achievements using fuzzy logic was developed in Python using the Google Colab
cloud service. In the process of analyzing educational data, the problems of
Data Mining are solved, since only some characteristics of the educational
process are obtained from a large sample of data. Data clustering was performed
using the classic K-Means method, which is characterized by simplicity and high
speed. Cluster analysis was performed in the space of two features using the
machine learning library scikit-learn (Python). The obtained clusters are
described by fuzzy triangular membership functions, which allowed to correctly
determine the membership of each student to a certain cluster. Creation of
fuzzy membership functions is done using the scikit-fuzzy library. The
development of fuzzy functions of objects belonging to clusters is also useful
for educational purposes, as it allows a better understanding of the principles
of using fuzzy logic. As a result of processing test educational data using the
developed software, correct results were obtained. It is shown that the use of
fuzzy membership functions makes it possible to correctly determine the
belonging of students to certain clusters, even if such clusters are not
clearly separated. Due to this, it is possible to more accurately determine the
recommended level of difficulty of tasks for each student, depending on his
previous evaluations.
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