Visualization of Learning Paths as Networks of Topics

Abstract: Nowadays, interactive visualizations have been one of the most used tools in Big Data fields for the purpose of searching for relationships and structured information in large datasets of unstructured information. In this project, these tools are applied to extract structured information from students following Self-Regulated Learning (SRL). By means of an interactive graph, we are able to study the paths that the students follow in the learning materials. Our visualization supports the investigation of patterns of behaviour of the students, which later could be used, for example, to adapt the study program to the student’s needs in a dynamic way or offer guidance if necessary.