Multi-instance genetic programming for predicting student performance in web based educational environments

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@Article{Zafra20122693,
  author =       "Amelia Zafra and Sebastian Ventura",
  title =        "Multi-instance genetic programming for predicting
                 student performance in web based educational
                 environments",
  journal =      "Applied Soft Computing",
  volume =       "12",
  number =       "8",
  pages =        "2693--2706",
  year =         "2012",
  ISSN =         "1568-4946",
  DOI =          "doi:10.1016/j.asoc.2012.03.054",
  URL =          "http://www.sciencedirect.com/science/article/pii/S1568494612001652",
  keywords =     "genetic algorithms, genetic programming, Educational
                 data mining, Multiple instance learning,
                 Classification",
  abstract =     "A considerable amount of e-learning content is
                 available via virtual learning environments. These
                 platforms keep track of learners' activities including
                 the content viewed, assignments submission, time spent
                 and quiz results, which all provide us with a unique
                 opportunity to apply data mining methods. This paper
                 presents an approach based on grammar guided genetic
                 programming, G3P-MI, which classifies students in order
                 to predict their final grade based on features
                 extracted from logged data in a web based education
                 system. Our proposal works with multiple instance
                 learning, a relatively new learning framework that can
                 eliminate the great number of missing values that
                 appear when the problem is represented by traditional
                 supervised learning. Experimental results are carried
                 out on data sets with information about several courses
                 and demonstrate that G3P-MI successfully achieves
                 better accuracy and yields trade-off between such
                 contradictory metrics as sensitivity and specificity
                 compared to the most popular techniques of multiple
                 instance learning. This method could be quite useful
                 for early identification of students at risk,
                 especially in very large classes, and allows the
                 instructor to provide information about the most
                 relevant activities to help students have a better
                 chance to pass a course.",
}

Genetic Programming entries for Amelia Zafra Gomez Sebastian Ventura

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