Learning Analytics Framework for Analysing Regulation in Collaborative Learning (FARCL)

Learning regulation research has advanced via the conceptualisation of socially shared regulation (SSRL) in collaborative learning. However, empirical evidencing of SSRL has still faced several challenges due to the unobservability of the cognitive and emotional processes at the core of regulation. Fortunately, with the aid of learning analytics and cutting-edge technologies for collecting and processing immense data from multiple modalities and channels, we are on the edge of revealing those “invisible” metacognitive level processes and also progressing in developing metrics for measuring core processes of regulation. Nevertheless, a systematic understanding of how theories on regulation in learning can inform learning analytics to maximise its potential is still lacking. Accordingly, the aim of this chapter is to discuss and demonstrate through examples how the socially shared regulation theoretical framework can be applied to the design of learning analytics. Hence, we propose a generic methodological Framework for Analysing Regulation in Collaborative Learning (FARCL) for the purpose of establishing a foundation for methodological advancement in measuring and examining regulation in collaborative learning environments. FARCL provides needed guidance for learning scientists and educational technology researchers to assess self-regulation, co-regulation, and socially shared regulation in collaborative learning.