Learning Analytics features an inherent interest in algorithms and computational methods of analysis. This makes Learning Analytics an interesting field of study for computer scientists and mathematically inspired researchers. A differentiated view of the different types of approaches is relevant not only for technology developers but also those involved for the design and interpretation of analytics applications. The spectrum of different analytic approaches ranges from analytics of (1) network structures including actor-actor (social) networks but also actor-artefact networks, (2) processes using methods of sequence analysis, and (3) content using text mining or other techniques of artefact analysis. Concerning network-based approaches (1), standard methods of Social Network Analysis (SNA), such as centrality measures or subcommunity detection, have received considerable attention in recent research. Still less known are approaches that use network analysis methods in combination with semantic networks and content analysis. Recent studies will be presented to exemplify challenges and potential benefits of using advanced computational methods that combine different methodological approaches.
Dr. H. Ulrich Hoppe holds a full professorship in the area “Collaborative and Learning Support Systems” at the University of Duisburg-Essen (Germany). After his PhD on interactive programming in math education in 1984, Ulrich Hoppe has worked for about ten years in the area of intelligent user interfaces and cognitive models in HCI, before he re-focused his research on intelligent support in educational systems and distributed collaborative environments in 1995. With his COLLIDE Research Group he has participated in more than ten European projects on Technology-enhanced learning. He was one of the initiators of the European Network of Excellence Kaleidoscope (2004-07). Currently he is engaged as a PI in a Research Training Group on “User Centred Social Media” funded by the German National Science Foundation since 2015. His research is focused on computational techniques for learning and knowledge building. He is particularly interested in combining network analysis techniques with other data mining methods in the study and support of online (learning) communities.