LAUSR

Abstract Explanation videos are increasingly common on media websites such as YouTube and are used by school students, university students, and members of the general public alike. Such videos cover all areas of knowledge and aim to provide viewer-appropriate explanations concerning a large variety of topics. It is, however, still far from clear how such videos work and under what conditions they are effective. In this paper, we consider how this can be measured and whether guidelines can be determined empirically for their improvement. Building on the notion of semantic waves developed in Legitimation Code Theory, we discuss the design of cumulative knowledge-building processes and how to isolate cases where this fails to operate, selecting examples from a number of videos of this kind. To support this, we introduce a detailed annotation framework that fully reflects the multimodally-rich extent of our data. This framework systematically defines coding categories for use with the ELAN annotation tool and uses these for the adjacent construction of multimodal cohesive chain diagrams. We first motivate and describe the application of this annotation framework and then show through subsequent multimodal cohesion analyses how constructed cohesive chains can be interpreted within the scope of pedagogically relevant semantic wave patterns.