Qualitative learning analytics to detect students’ emotional topography on EduOpen

What emotions can students experience in digitally mediated learning processes? In this paper, we connect Learning Analytics to the Grounded Theory in order to analyse the emotional world of students of 11 courses within the EduOpen (www.eduopen.org) massive open online course (MOOC) platform. Namely, we have used NVivo 11 Plus software and have adopted a bottom–up process to analyse the forum dedicated to students’ self-presentation from all the courses. Proceeding with the analysis, we defined a set of categories composed of a three-level system. At a more general level, we have two dimensions that we named, respectively, ‘Sentiments about shell’ and ‘Sentiments towards the pulp’. Each of these dimensions is composed of a number of ‘child’ categories and subcategories (which are the nodes in NVivo’s language). After defining the entire set of categories and categorising all the texts (which was a circular process), we run some graphs on NVivo showing the hierarchical structure of the dimensions, the relations between the dimensions and the sources and the clusters of dimensions by coding similarity. The results show how some courses are composed of more negative or more positive sentiments (towards the topic and/or the logistic arrangement of the course) and how the motivation dimension characterises the broad emotional dimension of students heavily. In an evidence-based action-research perspective, these results provide interesting suggestions to personalise the learning activities proposed to students by EduOpen.