“…These issues primarily relate to data collection (e.g., privacy, ethics, consent), modelling (e.g., inclusion, accuracy), algorithms (e.g., bias, accountability) (ACM US Public Policy Council, 2017), and data management (e.g., data ownership, provenance, storage), none of which are likely to be unique to collaboration analytics, but instead have ethical dimensions of analytics that require deliberate consideration and proactive planning by the broader learning analytics community (Drachsler et al, 2015;Wise, 2019). Some of these issues, which are present in the wider learning analytics and collaboration analytics research, are evident in the special issue articles, namely predictive modelling and the use of NLP (e.g., Lämsä et al, 2021). For instance, Lämsä and colleagues (2021) provide a novel exploration of face-to-face collaborative inquiry-based learning interactions.…”