Preface to the special issue on data mining for personalised educational systems

Abstract

Educational data mining (EDM) is an emerging interdisciplinary research area which deals with the development of methods for the exploration of data which have originated in an educational context (Baker andYacef 2009). EDMuses statistical, machine learning and data mining (DM) approaches to analyse educational data in order to study educational issues. On the one hand, DM has many applications, and many tasks in educational environments have been resolved through DM (Romero and Ventura 2010), such as the analysis and visualisation of data, providing feedback in order to support instructors, providing recommendations for students, student modelling, detecting undesirable student behaviours, predicting student performance, grouping students, social networks analysis, developing concept maps, constructing courseware, planning and scheduling, etc. In particular, this special issue of User Modelling and User-Adapted Interaction explores recent developments and applications of data mining techniques in various aspects of user modelling and the personalisation, recommendation and adaptation of educational systems. On the other hand, there are different types of educational systems, such as traditional classrooms, test/quiz systems, e-learning systems, learning management systems (LMS), adaptive educational hypermedia systems (AEHS), intelligent tutoring systems (ITS), etc. (Romero and Ventura 2007). In particular, AEHS and ITS are adaptive and intelligent educational systems which attempt to be more adaptable by building a model of the goals, preferences and knowledge of each individual student and using this model throughout the interaction with the student in order to adapt to their individual needs (Brusilovsky and Peylo 2003). These specific types of educational systems aim to fulfill the ultimate goal