To be able to collaborate, individuals need to have sufficient GA about others within their social environment. Although what is “sufficient” may severely depend on the task, it seems reasonable to assume that some level of GA is relevant for all targeted forms of social interaction. Research on the use of GA tools in CSCL assumes that GA affects how individuals interpret and behave in social situations (Janssen & Bodemer,
2013). Thus, guiding interaction processes is seen as a key function of GA and respective tools (see Bodemer,
2011) and it has also been related to (shared) regulatory activities (e.g., Järvelä et al.,
2016; Rojas et al.,
in press). Based on their current GA, individuals may decide how to act and react within social situations and how to collaborate. For example, GA information may support learners’ identification of knowledge gaps or conflicting assumptions within the group, thereby triggering learning and interaction processes to fill knowledge gaps or resolve conflict (e.g., Schnaubert & Bodemer,
2019). The script theory of guidance assumes these interaction processes are guided by internal collaboration scripts (Fischer et al.,
2013). While empirical research on the role of GA in script activation and regulation is scarce, it is assumed that awareness of social conditions may not only activate internal collaboration scripts, but may also play a role in regulating script implementation and adaptation (Schnaubert, et al.,
2020b). Thus, the act of monitoring social conditions in order to initiate and adapt interaction processes may be viewed as part of a regulatory cycle guiding collaborative learning and interaction processes. This may not only include monitoring socio-cognitive conditions to focus on resolving conflicts or controversies (e.g., Gijlers & de Jong,
2009) or guiding communication based on content-related prior knowledge (e.g., Erkens & Bodemer,
2019; Nückles et al.,
2005) or understanding (Dehler et al.,
2011), but may also include coordinating activities based on behavioral awareness (e.g., Janssen et al.,
2011; Strauß & Rummel,
2021) or regulating emotions and socio-emotional conflict (e.g., Eligio et al.,
2012; Järvenoja et al.,
2020; Näykki et al.,
2014). Some of these cited examples deploy static awareness tools not intended to catch the dynamics of collaboration (Engelmann et al.,
2009) and thus not suitable to support the full cycle of regulatory processes (e.g., by adapting or “fine-tuning” activities; see Buder,
2011). However, the underlying assumptions are that learners are not only guided to focus on relevant content, but also monitor resolution processes in order to terminate or adapt collaborative learning processes when required. These adaptations may involve self-, co- and shared regulation processes (Hadwin et al.,
2018; Järvelä & Hadwin,
2013), when learners perceive a deficit or misalignment between a partner’s and their own learning processes. For example, when noticing differences in task understanding, they may choose to co-regulate learning partners or negotiate a common strategy and awareness tools may support this process (e.g., Järvelä et al.,
2015; Kwon,
2020; Malmberg et al.,
2015). Thus, regulating social interaction based on GA may target interaction processes that serve co- and shared regulation during various phases in CSCL. Another example of adapting social interaction would be that when noticing a misalignment in understanding with regard to a key concept in the learning domain, learners may choose to discuss the concept, potentially leading to conflict resolution (Bodemer,
2011). For this, they may use various strategies, like presenting and justifying their own understanding (e.g., Melzner et al.,
2020). Once being aware of the conflict to be resolved, learners may terminate conflict resolution and move on to a different topic (e.g., Bodemer & Scholvien,
2014). Alternatively, through monitoring group understanding, they may become aware that their resolution strategies do not work and choose to adapt their interaction processes accordingly. Thus, GA serves a similar function in collaborative learning as metacognition serves within the individual, however, it may relate to group functioning on a relational level as well as learning processes within the content space (Janssen & Bodemer,
2013). Apart from acutely regulating collaborative learning and interaction processes, learners may thereby gain metacognitive knowledge on usefulness of the resolution or learning strategy. Through such processes, they may adapt their internal collaboration scripts, considering not only the deployed strategies and their success, but also conditional knowledge gained from being aware of social conditions during collaboration. This type of regulation is illustrated by cycle (2) in Fig.
2.