|
Descripción
|
|
|---|---|
| This paper presents a predictive student action model, 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 considering 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 student and the total time that each student spent to complete the entire practice. Next, for each cluster an extended automata is created, which allows us to generate predictions more reliable to each student type. In turn, the action prediction based on this model helps an intelligent tutoring system to generate students? feedback proactively. | |
|
Internacional
|
Si |
|
Nombre congreso
|
8th International Conference on Educational Data Mining EDM 2015 |
|
Tipo de participación
|
960 |
|
Lugar del congreso
|
Madrid, España |
|
Revisores
|
Si |
|
ISBN o ISSN
|
978-84-606-9425-0 |
|
DOI
|
|
|
Fecha inicio congreso
|
26/06/2015 |
|
Fecha fin congreso
|
29/06/2015 |
|
Desde la página
|
614 |
|
Hasta la página
|
615 |
|
Título de las actas
|
Proceedings of the 8th International Conference on Educational Data Mining |