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.

中文翻译：

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使用多模态学习分析的协同定位协同建模的文献综述——我们能走整整九码吗？

协作是 21 世纪重要的技能之一。它可以发生在远程或协同定位的环境中。同地协作 (CC) 是一个非常复杂的过程，涉及微妙的人类交互，可以用不同方式的眼睛注视、说话时间、音调和社交技能等指标来描述。随着传感器的出现，多模态学习分析获得了检测 CC 质量的动力。指标（或低级事件）可用于在可测量标记（即由一个或多个指标组成的索引）的帮助下检测 CC 质量，这些标记给出高级协作过程定义。但是，如果不考虑 CC 的场景（例如解决问题或会议），这种理解是不完整的。CC 的情景会影响所考虑的一组指标：例如，在协作编程中，从合作伙伴那里抢鼠标是协作的标志；而在协作会议中，眼睛注视和音频级别是协作的指标。这可能是每个场景中不同目标和基本参数（例如群体行为、交互或组合）的结果。在本文中，我们基于场景驱动的优先级展示了我们在指标配置文件方面的工作，将不同 CC 场景中的参数映射到指标和可用索引上。这定义了概念模型以支持 CC 质量检测和预测系统的设计。这可能是每个场景中不同目标和基本参数（例如群体行为、交互或组合）的结果。在本文中，我们基于场景驱动的优先级展示了我们在指标配置文件方面的工作，将不同 CC 场景中的参数映射到指标和可用索引上。这定义了概念模型以支持 CC 质量检测和预测系统的设计。这可能是每个场景中不同目标和基本参数（例如群体行为、交互或组合）的结果。在本文中，我们基于场景驱动的优先级展示了我们在指标配置文件方面的工作，将不同 CC 场景中的参数映射到指标和可用索引上。这定义了概念模型以支持 CC 质量检测和预测系统的设计。