Learning Analytics for Understanding and Supporting Collaboration

By Bodong Chen and Stephanie D. Teasley in publications

March 21, 2022


March 21, 2022


12:00 AM


Collaboration is an important competency in the modern society. To harness the intersection of learning, work, and collaboration with analytics, several fundamental challenges need to be addressed. This chapter about collaboration analytics aims to highlight these challenges for the learning analytics community. We first survey the conceptual landscape of collaboration and learning with a focus on the computer-supported collaborative learning (CSCL) literature while attending to perspectives from computer supported cooperative work (CSCW). Grounded in the conceptual exploration, we then distinguish two salient strands of collaboration analytics: (a) computational analysis of collaboration that applies computational methods to examining collaborative processes; and (b) analytics for collaboration which is primarily concerned with designing and deploying data analytics in authentic contexts to facilitate collaboration. Examples and cases representing different contexts for learning and analytical frames are presented, followed by a discussion of key challenges and future directions.

Posted on:
March 21, 2022
1 minute read, 144 words
learning analytics collaborative learning
See Also:
Network Motifs as Codes
Socio-Semantic Network Motifs Framework for Discourse Analysis
Socio-Temporal Dynamics in Peer Interaction Events