Data Mining for Education is an emerging discipline which seeks to develop methods to explore large amounts of data from educational settings, in order to understand students better. In recent years there have been various works related with this specialty and have used multiple data mining techniques derived from this to address different educational problems. The aim of this publication is to present a review of the works in which data mining techniques were used to solve specific problems of education and to do a classification of these, associated with diverse scenarios in which it has been applied.Resumen: La minería de datos para la educación es una disciplina emergente la cual busca el desarrollo de métodos que permitan explorar grandes cantidades de datos provenientes de entornos educativos con el fin de entender mejor a los estudiantes. En los últimos años se han realizado diversos trabajos relacionados con esta disciplina y se han utilizado múltiples técnicas de minería de datos para abordar diferentes problemáticas educativas. El objetivo de esta publicación es presentar una revisión de los trabajos en los cuales se han utilizado técnicas de minerías de datos para solucionar problemáticas particulares de la educación y realizar una clasificación de éstas asociadas a los diversos escenarios en los que se han aplicado.

The paper assumes Philosophy of Technology importance in Affective Computing and Social Network Analysis from three perspectives: challenges generated by changes in relationships stablished in social networks, contemporary interactions nature between artifacts, humans and environments throughout concepts as meaning and experience and applying data mining to enhance information comprehension, reflection and influence in contemporary knowledge societies. Through a flexible methodology of comparing concepts presented in literature to expert's viewpoints, it outlines some of the key ethical issues around analysis of social interactions shared in the web.