Collaboration is one of the important 21st-century skills. It can take place in remote or co-located settings. Co-located collaboration (CC) is a very complex process that involves subtle human interactions that can be described with indicators like eye gaze, speaking time, pitch, and social skills from different modalities. With the advent of sensors, multimodal learning analytics has gained momentum to detect CC quality. Indicators (or low-level events) can be used to detect CC quality with the help of measurable markers (i.e., indexes composed of one or more indicators) which give the high-level collaboration process deﬁnition. However, this understanding is incomplete without considering the scenarios (such as problem solving or meetings) of CC. The scenario of CC affects the set of indicators considered: for instance, in collaborative programming, grabbing the mouse from the partner is an indicator of collaboration; whereas in collaborative meetings, eye gaze, and audio level are indicators of collaboration. This can be a result of the differing goals and fundamental parameters (such as group behavior, interaction, or composition) in each scenario. In this review, we present our work on proﬁles of indicators on the basis of a scenario-driven prioritization, the parameters in different CC scenarios are mapped onto the indicators and the available indexes. This deﬁnes the conceptual model to support the design of a CC quality detection and prediction system.
Reference: Open Access!!
Praharaj, S., Scheffel, M., Drachsler H. and Specht M. M. (2021). “Literature Review on Co-Located Collaboration Modeling Using Multimodal Learning Analytics. Can We Go the Whole Nine Yards?,” in IEEE Transactions on Learning Technologies, doi: 10.1109/TLT.2021.3097766.