Predicting Academic Achievement Using Multiple Instance Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@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