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.

Contact: Sebastián Ventura Soto

Related Researchers:


  Name Email Area Cat.
Luna Ariza, Jose María Data Science and Big Data Area PhD
Luna Ariza, Jose María jmluna@uco.CI0utRaAKjNes Data Science and Big Data Area PhD
Ortíz García, Andrés DaSCI Technology Applications Area PhD
Romero Morales, Cristobal Data Science and Big Data Area PhD
Ventura Soto, Sebastián sventura@uco.eFFZDq@eE4es Data Science and Big Data Area PhD
Zafra Gómez, Amelia azafra@uco.VAwJGxVpD0zaes Data Science and Big Data Area PhD