@unpublished{upm41758,
            year = {2015},
            note = {Unpublished},
         address = {Madrid},
           title = {A model for student action prediction in 3D virtual environments for procedural training},
       booktitle = {EDM 2015: proceedings of the 8th International Conference on Educational Data Mining},
          volume = {1},
           pages = {614--615},
       publisher = {Universidad Nacional de Educaci{\'o}n a Distancia},
             url = {http://www.educationaldatamining.org/EDM2015/index.php?page=proceedings},
        keywords = {Intelligent Tutoring Systems; Educational Data Mining; Elearning; Procedural training; Virtual environments},
          author = {Riofr{\'i}o Luzcando, Diego and Ram{\'i}rez Rodr{\'i}guez, Jaime},
            isbn = {978-84-606-9425-0},
        abstract = {This paper presents a predictive student actionmodel, which uses student logs generated by a 3D virtual environment for procedural training to elaborate summarized information. This model can predict the most common behaviors by con- sidering the sequences of more frequent actions, which is useful to anticipate common student? errors. These logs are clustered based on the number of errors made by each stu- dent and the total time that each student spent to complete the entire practice. Next, for each cluster an extended au- tomata is created, which allows us to generate predictions more reliable to each student type. In turn, the action pre- diction based on this model helps an intelligent tutoring sys- tem to generate students? feedback proactively.}
}