Predicting Academic Achievement Using Multiple Instance Genetic Programming
Created by W.Langdon from
gp-bibliography.bib Revision:1.8081
- @InProceedings{Zafra:2009:ISDA,
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author = "Amelia Zafra and Cristobal Romero and
Sebastian Ventura",
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title = "Predicting Academic Achievement Using Multiple
Instance Genetic Programming",
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booktitle = "Ninth International Conference on Intelligent Systems
Design and Applications, ISDA '09",
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year = "2009",
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month = "30 2009-" # dec # " 2",
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pages = "1120--1125",
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keywords = "genetic algorithms, genetic programming, G3P-MI,
academic achievement prediction, grammar guided genetic
programming algorithm, multiple instance genetic
programming, multiple instance learning, student
performance prediction, university-level learning,
computer aided instruction",
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DOI = "doi:10.1109/ISDA.2009.108",
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abstract = "The ability to predict a student's performance could
be useful in a great number of different ways
associated with university-level learning. In this
paper, a grammar guided genetic programming algorithm,
G3P-MI, has been applied to predict if the student will
fail or pass a certain course and identifies activities
to promote learning in a positive or negative way from
the perspective of MIL. Computational experiments
compare our proposal with the most popular techniques
of multiple instance learning (MIL). Results show that
G3P-MI achieves better performance with more accurate
models and a better trade-off between such
contradictory metrics as sensitivity and specificity.
Moreover, it adds comprehensibility to the knowledge
discovered and finds interesting relationships that
correlate certain tasks and the time devoted to solving
exercises with the final marks obtained in the
course.",
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notes = "Also known as \cite{5364212}",
- }
Genetic Programming entries for
Amelia Zafra Gomez
Cristobal Romero Morales
Sebastian Ventura
Citations