Collaboration is one of the four important 21st-century skills. With the pervasive use of sensors, interest on co-located collaboration (CC) has increased lately. Most related literature used the audio modality to detect indicators of collaboration (such as total speaking time and turn taking). CC takes place in physical spaces where group members share their social (i.e., non-verbal audio indicators like speaking time, gestures) and epistemic space (i.e., verbal audio indicators like the content of the conversation). Past literature has mostly focused on the social space to detect the quality of collaboration. In this study, we focus on both social and epistemic space with an emphasis on the epistemic space to understand different evolving collaboration patterns and collaborative convergence and quantify collaboration quality. We conduct field trials by collecting audio recordings in 14 different sessions in a university setting while the university staff and students collaborate over playing a board game to design a learning activity. This collaboration task consists of different phases with each collaborating member having been assigned a pre-fixed role. We analyze the collected group speech data to do role-based profiling and visualize it with the help of a dashboard.

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 article, 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.

Collaboration is an important 21st century skill; it can take place in a remote or co-located setting. Co-located collaboration (CC) gives rise to subtle human interactions that can be described with multimodal indicators like gaze, speech and social skills. In this demo paper, we first give a brief overview of related work that has identified indicators during CC. Then, we look briefly at the feedback mechanisms that have been designed based on these indicators to facilitate CC. Using these theoretical insights, we design a prototype to give automated real-time feedback to facilitate CC taking the help of the most abundant modality during CC i.e., audio cues.

Collaboration is an important 21st century skill; it can take place in a remote or co-located setting. Co-located collaboration (CC) is a very complex process which involves subtle human interactions that can be described with multimodal indicators (MI) like gaze, speech and social skills. In this paper, we first give an overview of related work that has identified indicators during CC. Then, we look into the state-of-the-art studies on feedback during CC which also make use of MI. Finally, we describe a Wizard of Oz (WOz) study where we design a privacy-preserving research prototype with the aim to facilitate real-time collaboration in-the-wild during three co-located group PhD meetings (of 3–7 members). Here, human observers stationed in another room act as a substitute for sensors to track different speech-based cues (like speaking time and turn taking); this drives a real-time visualization dashboard on a public shared display. With this research prototype, we want to pave way for design-based research to track other multimodal indicators of CC by extending this prototype design using both humans and sensors.