The Why, the What and the How to Model a Dynamic Relational Learning Process with Temporal Networks

Abstract

Research on online learning has benefited from intensive data collection to understand students’ online behavior and performance. Several learning analytics techniques have been operationalized to examine the temporal nature of learning that includes changes, phases, and sequences of students’ online actions. Moreover, to account for the relational nature of learning, researchers have harnessed the power of network analysis to model the relational dimensions of data, mapping connections between learners and resources, and discovering interacting communities. However, prior research has rarely combined the two aspects (temporal and relational), but rather most researchers rely on aggregate networks where the time dimension has been ignored. To combine both these aspects, temporal networks provide a rich framework of statistical and visualization techniques that allow to fully understand, for instance, the evolution and building up of learning communities, the sequence of co-construction of knowledge, the flow of information, and the building of social capital, to name a few examples. Since temporal networks have been rarely used in educational research, with this study, we aim to provide an introduction to this method, with an emphasis on the differences with conventional static networks. We explain the basics of temporal networks, the different subtypes thereof, and the measures that can be taken, as well as examples from the few existing prior works. © 2022 Copyright for this paper by its authors.