  Using learning analytics to investigate and support collaborative learning
has been explored for many years. Recently, automated approaches with various
artificial intelligence approaches have provided promising results for
modelling and predicting student engagement and performance in collaborative
learning tasks. However, due to the lack of transparency and interpretability
caused by the use of "black box" approaches in learning analytics design and
implementation, guidance for teaching and learning practice may become a
challenge. On the one hand, the black box created by machine learning
algorithms and models prevents users from obtaining educationally meaningful
learning and teaching suggestions. On the other hand, focusing on group and
cohort level analysis only can make it difficult to provide specific support
for individual students working in collaborative groups. This paper proposes a
transparent approach to automatically detect student's individual engagement in
the process of collaboration. The results show that the proposed approach can
reflect student's individual engagement and can be used as an indicator to
distinguish students with different collaborative learning challenges
(cognitive, behavioural and emotional) and learning outcomes. The potential of
the proposed collaboration analytics approach for scaffolding collaborative
learning practice in face-to-face contexts is discussed and future research
suggestions are provided.
