Exploring students' interaction in MOOCS via learning analytics

Online distance learning, especially in Massive Open Online Courses (MOOCs), poses some challenges for managing a large-scale interaction taking place among a high number of students. Learning Analytics (LA) offers tools that can help overcome these challenges by providing relevant information about student engagement patterns and their modes of interaction. This paper presents a case study where the LA system of the EduOpen platform was applied for exploring and interpreting students’ interaction patterns in an Academic Italian Language MOOC Italiano accademico per studenti slavofoni. Data analysis from the course first edition has revealed the usefulness of Learning Analytics techniques for providing information on learners’ involvement, which would otherwise remain unobserved. The relation between the data extracted from the learning system’s activity logs and student performance has been investigated to verify the conditions required for potentiating academic performance. The findings show that Learning Analytics proves to be effective as a MOOC analysis methodology as it can help to manage students’ interaction and improve massive online language learning though further research is needed to verify reliability of its parameters for predictive purposes. The results have led to implementing some solutions while proposing an innovative approach for online interaction in Language MOOCs.