Educational Science Faculty, Open University of the Netherlands, 6419 AT Heerlen, The Netherlands.
Institute of Education Science, Ruhr-Universität Bochum, 44801 Bochum, Germany.
Sensors (Basel). 2021 May 2;21(9):3156. doi: 10.3390/s21093156.
Collaboration is an important 21st Century skill. Co-located (or face-to-face) collaboration (CC) analytics gained momentum with the advent of sensor technology. Most of these works have used the audio modality to detect the quality of CC. The CC quality can be detected from simple indicators of collaboration such as total speaking time or complex indicators like synchrony in the rise and fall of the average pitch. Most studies in the past focused on "how group members talk" (i.e., spectral, temporal features of audio like pitch) and not "what they talk". The "what" of the conversations is more overt contrary to the "how" of the conversations. Very few studies studied "what" group members talk about, and these studies were lab based showing a representative overview of specific words as topic clusters instead of analysing the richness of the content of the conversations by understanding the linkage between these words. To overcome this, we made a starting step in this technical paper based on field trials to prototype a tool to move towards automatic collaboration analytics. We designed a technical setup to collect, process and visualize audio data automatically. The data collection took place while a board game was played among the university staff with pre-assigned roles to create awareness of the connection between learning analytics and learning design. We not only did a word-level analysis of the conversations, but also analysed the richness of these conversations by visualizing the strength of the linkage between these words and phrases interactively. In this visualization, we used a network graph to visualize turn taking exchange between different roles along with the word-level and phrase-level analysis. We also used centrality measures to understand the network graph further based on how much words have hold over the network of words and how influential are certain words. Finally, we found that this approach had certain limitations in terms of automation in speaker diarization (i.e., who spoke when) and text data pre-processing. Therefore, we concluded that even though the technical setup was partially automated, it is a way forward to understand the richness of the conversations between different roles and makes a significant step towards automatic collaboration analytics.
协作是 21 世纪的一项重要技能。随着传感器技术的出现，同地（或面对面）协作（CC）分析得到了发展。这些工作大多使用音频模态来检测 CC 的质量。CC 质量可以通过简单的协作指标来检测，例如总发言时间，也可以通过复杂的指标来检测，例如平均音高的上升和下降的同步性。过去的大多数研究都集中在“团队成员如何交谈”（即音频的频谱、时域特征，如音高）上，而不是“他们在谈论什么”上。对话的“内容”比对话的“方式”更明显。很少有研究探讨团队成员谈论的内容，这些研究是基于实验室的，展示了特定词作为话题群的代表性概述，而不是通过理解这些词之间的联系来分析对话内容的丰富性。为了克服这一问题，我们在这项基于实地试验的技术论文中迈出了第一步，旨在开发一种工具，以迈向自动协作分析。我们设计了一种技术设置，以自动收集、处理和可视化音频数据。数据收集是在大学工作人员玩棋盘游戏时进行的，他们被预先分配了角色，以提高对学习分析和学习设计之间联系的认识。我们不仅对对话进行了词级分析，还通过交互式可视化这些词和短语之间的联系强度来分析这些对话的丰富性。在这种可视化中，我们使用网络图来可视化不同角色之间的轮次交换，以及词级和短语级分析。我们还使用中心性度量来进一步理解网络图，了解词在词网中的影响力以及某些词的影响力。最后，我们发现这种方法在说话人角色分配（即谁在何时发言）和文本数据预处理方面存在一定的自动化局限性。因此，我们得出结论，尽管技术设置在自动化方面存在一定的局限性，但这是理解不同角色之间对话丰富性的一种方法，也是迈向自动协作分析的重要一步。
