Group Leader vs. Remaining Group—Whose Data Should Be Used for Prediction of Team Performance?

IF 2.4 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Multimodal Technologies and Interaction Pub Date : 2023-09-14 DOI:10.3390/mti7090090
Ronald Böck
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Abstract

Humans are considered to be communicative, usually interacting in dyads or groups. In this paper, we investigate group interactions regarding performance in a rather formal gathering. In particular, a collection of ten performance indicators used in social group sciences is used to assess the outcomes of the meetings in this manuscript, in an automatic, machine learning-based way. For this, the Parking Lot Corpus, comprising 70 meetings in total, is analysed. At first, we obtain baseline results for the automatic prediction of performance results on the corpus. This is the first time the Parking Lot Corpus is tapped in this sense. Additionally, we compare baseline values to those obtained, utilising bidirectional long-short term memories. For multiple performance indicators, improvements in the baseline results are able to be achieved. Furthermore, the experiments showed a trend that the acoustic material of the remaining group should use for the prediction of team performance.
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团队领导与剩余团队——谁的数据应该被用来预测团队绩效?
人类被认为是善于交流的,通常是两个人或一群人互动。在本文中,我们研究了在一个相当正式的聚会中有关表演的群体互动。特别是,在社会群体科学中使用的十个绩效指标的集合被用来评估本手稿中会议的结果,以自动的,基于机器学习的方式。为此,对停车场语料库进行了分析,该语料库共包括70个会议。首先,我们获得了在语料库上自动预测性能结果的基线结果。这是停车场语料库第一次在这个意义上被挖掘。此外,我们将基线值与利用双向长短期记忆获得的值进行比较。对于多个性能指标,可以实现基线结果的改进。此外,实验还显示了一种趋势,即剩余群体的声学材料应该用于预测团队表现。
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来源期刊
Multimodal Technologies and Interaction
Multimodal Technologies and Interaction Computer Science-Computer Science Applications
CiteScore
4.90
自引率
8.00%
发文量
94
审稿时长
4 weeks
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