Towards collaboration literacy development through multimodal learning analytics

Abstract:

The last ten years has involved significant growth and development in the learning analytics community. One of the developments to recently emerge as a recognized special interest group in Learning Analytics is the sub-field of Multimodal Learning Analytics (MmLA). In this paper we consider a future trajectory for MmLA that intersects with the cross-cutting 21st century skill of collaboration. Teaching collaboration is seldom the focus of formal, or informal learning experiences, as students and teachers rarely receive feedback on their collaboration process. Instead, feedback is normally reduced to an outcome measure, or requires a level of human analysis that is intractable at scale. We see a unique opportunity for MmLA to promote collaboration literacy, and for collaboration literacy to be a common space in which to grow MmLA. Concretly, MmLA can provide the theoretical and technological innovations needed to create tools that support the evaluation, assessment and development of collaborative skills. As a first step in this direction, this paper presents a framework for collaboration literacy that consists of four levels of increasing complexity. We describe examples of current work in the first three levels of the framework, and situate the fourth level as an aspirational goal for the field of MmLA. We also discuss some of the key challenges that need to be solved to facilitate increased adoption of a collaboration literacy feedback tool, and MmLA more broadly. Ultimately, we argue that the development of such a tool could be instrumental in introducing new ways for building collaboration literacy.

Año de publicación:

2020

Keywords:

• data fusion
• data capture
• Multimodal feedback
• framework

Fuente:

scopus