Mining coherent evolution patterns in education through biclustering

04/07/2014

With the spread of information systems and the increased interest in education, the quantity of data about education has exploded along with a new field - Educational Data Mining. Predicting students’ performance has been approached by several techniques, but the combination of supervised and non-supervised techniques appeared as a new tool for improving the results. Biclustering algorithms have been successfully applied in areas such as gene expression data and information retrieval, but not used in the educational context. In this paper, we show how to apply biclustering techniques to educational data and to use its results as features to improve the prediction of student’s performance

Autores da comunidade :

• 

  A

  André Carlos Rita do Vale

  ist162604

• 

  S

  Sara Alexandra Cordeiro Madeira

  ist46399

  Afiliações e fontes

  IST > INESC-ID

  Instituto de Engenharia de Sistemas e Computadores: Investigação e Desenvolvimento em Lisboa

• 

  C

  Claudia Martins Antunes

  ist14046

  Afiliações e fontes

  IST > DEI

  Departamento de Engenharia Informática

  De: dblp

  ID: conf/edm/ValeMA14

Educational Data Mining Society

https://www.educationaldatamining.org/EDM2014/uploads/procs2014/posters/67_EDM-2014-Poster.pdf

7th International Conference on Educational Data Mining (EDM 2014)

London, UK

04/07/2014

07/07/2014

391

392

computer-and-information-sciences - Ciências da Computação e da Informação

eng - Inglês