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A model for student action prediction in 3D virtual environments for procedural training
Cita
Riofrío Luzcando, Diego and Ramírez Rodríguez, Jaime 
ORCID: https://orcid.org/0000-0003-4006-698X
(2015).
A model for student action prediction in 3D virtual environments for procedural training.
En: "8th International Conference on Educational Data Mining", 26-29 Jun 2015, Madrid, España. ISBN 978-84-606-9425-0. pp. 614-615.
ORCID: https://orcid.org/0000-0003-4006-698X
(2015).
A model for student action prediction in 3D virtual environments for procedural training.
En: "8th International Conference on Educational Data Mining", 26-29 Jun 2015, Madrid, España. ISBN 978-84-606-9425-0. pp. 614-615.
Descripción
| Título: | A model for student action prediction in 3D virtual environments for procedural training |
|---|---|
| Autor/es: |
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| Tipo de Documento: | Ponencia en Congreso o Jornada (Otro) |
| Título del Evento: | 8th International Conference on Educational Data Mining |
| Fechas del Evento: | 26-29 Jun 2015 |
| Lugar del Evento: | Madrid, España |
| Título del Libro: | EDM 2015: proceedings of the 8th International Conference on Educational Data Mining |
| Fecha: | 2015 |
| ISBN: | 978-84-606-9425-0 |
| Volumen: | 1 |
| Materias: | |
| ODS: | |
| Palabras Clave Informales: | Intelligent Tutoring Systems; Educational Data Mining; Elearning; Procedural training; Virtual environments |
| Escuela: | E.T.S. de Ingenieros Informáticos (UPM) |
| Departamento: | Inteligencia Artificial |
| Licencias Creative Commons: | Reconocimiento - Sin obra derivada - No comercial |
Resumen
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.
Más información
| ID de Registro: | 41758 |
|---|---|
| Identificador DC: | https://oa.upm.es/41758/ |
| Identificador OAI: | oai:oa.upm.es:41758 |
| URL Oficial: | http://www.educationaldatamining.org/EDM2015/index... |
| Depositado por: | Memoria Investigacion |
| Depositado el: | 18 Ene 2017 09:39 |
| Ultima Modificación: | 18 Ene 2017 09:39 |
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