Predicting Student Grades in Learning Management Systems with Multiple Instance Learning Genetic Programming

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

@InProceedings{conf/edm/ZafraV09,
  title =        "Predicting Student Grades in Learning Management
                 Systems with Multiple Instance Learning Genetic
                 Programming",
  author =       "Amelia Zafra and Sebastian Ventura",
  bibdate =      "2010-10-06",
  bibsource =    "DBLP,
                 http://dblp.uni-trier.de/db/conf/edm/edm2009.html#ZafraV09",
  booktitle =    "Educational Data Mining - {EDM} 2009, Cordoba, Spain,
                 July 1-3, 2009. Proceedings of the 2nd International
                 Conference on Educational Data Mining",
  publisher =    "http://www.educationaldatamining.org",
  year =         "2009",
  editor =       "Tiffany Barnes and Michel C. Desmarais and 
                 Crist{\'o}bal Romero and Sebasti{\'a}n Ventura",
  isbn13 =       "978-84-613-2308-1",
  pages =        "309--318",
  URL =          "http://www.educationaldatamining.org/EDM2009/uploads/proceedings/zafra.pdf",
  URL =          "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.209.93",
  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 Multiple Instance Learning (MIL).
                 Computational experiments compare our proposal with the
                 most popular techniques of 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.",
  keywords =     "genetic algorithms, genetic programming",
}

Genetic Programming entries for Amelia Zafra Gomez Sebastian Ventura

Citations