Educational Data Mining
Educational Data Mining (EDM) and Learning Analytics (LA) are research areas that aim to develop methods for measuring, collecting, exploiting, and analyzing data from educational environments to solve critical educational issues and problems. This research area, which began to be developed at the beginning of 2000, has grown enormously, successfully addressing student modeling problems, predicting academic performance, personalizing teaching, detecting abnormal behavior, recommending resources, or automatically constructing courses. Some of the most recent interest areas are related to improving the models obtained in terms of transferability, effectiveness, interpretability, applicability, and generality. On the other hand, with the appearance of new teaching systems that make use of multiple methodologies, there is a growing interest in the use of multimodal data, as well as the use of data from environmental and personal sensors that even allow access to students’ brain data, providing information on aspects such as their level of attention and concentration during classes.
Responsible: Sebastián Ventura Soto
|Zafra Gómez, Amelia||azafraguI.P6_tFz@uco.es||Data Science and Big Data Area||PhD|
|Romero Morales, Cristobal||cromero@zXxmaguco.es||Data Science and Big Data Area||PhD|
|Luna Ariza, Jose María||jmlunaqUkyIT36Kp@uco.es||Data Science and Big Data Area||PhD|
|Ventura Soto, Sebastián||sventura@uc4_sgS4C5o.es||Data Science and Big Data Area||PhD|