Autonomous Cycle of Data Analysis Tasks for Learning Processes

The data analysis has become a fundamental area for knowledge discovery from data extracted from different sources. In that sense, to develop mechanisms, strategies, methodologies that facilitate their use in different contexts, it has become an important need. In this paper, we propose an "Autonomic Cycle Of Data Analysis Tasks" for learning analytic (ACODAT) in the context of online learning environments, which defines a set of tasks of data analysis, whose objective is to improve the learning processes. Each data analysis task interacts with each other, and has different roles: observe the process, analyze and interpret what happens in it, or make decisions in order to improve the learning process. In this paper, we study the application of the autonomic cycle into the contexts of a smart classroom and a virtual learning platform.