Using data mining techniques for exploring learning object repositories

Purpose - This paper aims to show the results obtained from the data mining techniques application to learning objects (LO) metadata. Design/methodology/approach - A general review of the literature was carried out. The authors gathered and pre-processed the data, and then analyzed the results of data mining techniques applied upon the LO metadata. Findings - It is possible to extract new knowledge based on learning objects stored in repositories. For example it is possible to identify distinctive features and group learning objects according to them. Semantic relationships can also be found among the attributes that describe learning objects. Research limitations/implications - In the first section, four test repositories are included for case study. In the second section, the analysis is focused on the most complete repository from the pedagogical point of view. Originality/value - Many publications report results of analysis on repositories mainly focused on the number, evolution and growth of the learning objects. But, there is a shortage of research using data mining techniques oriented to extract new semantic knowledge based on learning objects metadata.