Predicting Academic Achievement Using Multiple Instance Genetic Programming

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@InProceedings{Zafra:2009:ISDA,

• author = "Amelia Zafra and Cristobal Romero and Sebastian Ventura",
• title = "Predicting Academic Achievement Using Multiple Instance Genetic Programming",
• booktitle = "Ninth International Conference on Intelligent Systems Design and Applications, ISDA '09",
• year = "2009",
• month = "30 2009-" # dec # " 2",
• pages = "1120--1125",
• 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",
• DOI = "doi:10.1109/ISDA.2009.108",
• 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.",
• notes = "Also known as \cite{5364212}",

}

Genetic Programming entries for Amelia Zafra Gomez Cristobal Romero Morales Sebastian Ventura

Citations