Experimenting Learning Analytics and Educational Data Mining in different educational contexts and levels

This paper seeks to present the similarities and differences of the practical application of Learning Analitycs and Educational Data Mining in different educational contexts and levels. To accomplish such objective this paper explains the theories involved, as well as the methodologies, processes, methods and the context of the applications. Therefore, three distinct use cases and practical applications developed are presented for the following scenarios: face-to-face secondary education, university education and technical secondary distance education. In this context, we seek to generate practical methodologies for exploring data from different educational databases and contexts, focusing on generating early warning models for school dropout. Thus, this paper presents the results obtained in these applications, the similarities and differences among them according 19 technical aspects (existence of temporality in the data, size of the databases, existence of multiple data sources, techniques used, among others). Lastly, the thesis establishes the scientific contribution and future work related to the topic.