Using a Learning Analytics Tool for Evaluation in Self-Regulated Learning

In self-regulated learning, evaluation is a complex task of the teaching process, but even more if students have social media that allow them to build their personal learning environment in different ways. In these kind of virtual environments a large amount of data that needs to be assessed by teachers is generated, and therefore they require tools that facilitate the assessment task. In this paper, we present an experiment with a process mining-based learning analytics tool, called SoftLearn, that helps teachers to assess the student's activity in self-regulated learning. The subject of this experiment is taught in blended learning mode with weekly classroom sessions, and the students use a social network software, called ELGG, as an e-portfolio in which they reflect their individual knowledge process construction. The results show that the use of this tool reduces significantly the assessment time and helps teachers to understand the learning process of the students.