Contributions to real-time monitoring and analysis of heterogeneous learning environments

Abstract

The ubiquity and flexibility of heterogeneous learning environments allows gathering a huge amount of data from students’ interactions. Applying learning analytics and data mining to these data, as well as a self-regulated learning criterion, is a well-accepted method to learn students’ behavior and ultimately predict their learning outcomes. In order to enrich the learning experience, the prediction should be done before the failure occurs. Thus, the thesis proposed in this paper aims to contribute with several prediction algorithms based in students’ interactions gathered through events in a real-time basis. This could be used to early detect students at risk and help them to succeed.