Utilizando comitês de classificadores para predição de rendimento escolar
Educational Data Mining is an application domain in artificial intelligence area that has been extensively explored nowadays. Technological advances and in particular, the increasing use of virtual learning environments have allowed the generation of considerable amounts of data to be investigate...
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| Formato: | Dissertação |
| Idioma: | por |
| Publicado em: |
Universidade Federal do Rio Grande do Norte
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| Endereço do item: | https://repositorio.ufrn.br/jspui/handle/123456789/19928 |
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| Resumo: | Educational Data Mining is an application domain in artificial intelligence
area that has been extensively explored nowadays. Technological advances and
in particular, the increasing use of virtual learning environments have allowed the
generation of considerable amounts of data to be investigated. Among the
activities to be treated in this context exists the prediction of school performance
of the students, which can be accomplished through the use of machine learning
techniques. Such techniques may be used for student’s classification in predefined
labels. One of the strategies to apply these techniques consists in their
combination to design multi-classifier systems, which efficiency can be proven by
results achieved in other studies conducted in several areas, such as medicine,
commerce and biometrics. The data used in the experiments were obtained from
the interactions between students in one of the most used virtual learning
environments called Moodle. In this context, this paper presents the results of
several experiments that include the use of specific multi-classifier systems
systems, called ensembles, aiming to reach better results in school performance
prediction that is, searching for highest accuracy percentage in the student’s
classification. Therefore, this paper presents a significant exploration of
educational data and it shows analyzes of relevant results about these
experiments. |
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