Learning Analytics Models: A Brief Review

摘要

The users of the World Wide Web produce data continuously. This happens in varied areas such as trading on line, product ratings, support and use of services, and many more, comprising Distance Education. The ever increasing amount of such data can make analysis and extraction of meaningful information progressively harder, and sophisticated analysis techniques are to be used to extract added value from data. Many companies do collection and analysis of data with the purpose to develop their marketing strategies. In the field of education, and Distance Education in particular, data collected through online Learning Management Systems (LMSs) can provide a great resource, and a strong challenge, for the analysis of learning processes, the design of training paths, and the updating and personalization of learning environments. While, on the one hand, there is an increasing demand by educational institutions to measure, demonstrate, and improve the results achieved in distance learning, on the other hand the logic of traditional reporting included in LMS platforms does not satisfy that growing need. Learning Analytics is the answer to the need for optimization of learning through the techniques of analysis of data produced by learning processes, involving all stakeholders of the system. In this paper we show and discuss a brief state of the art of models of Learning Analytics presented in the literature.