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<p>The increasing availability of multimodal sensing technologies has opened new avenues for studying human interactions. However, there remains a lack of systematic synthesis regarding which multimodal metrics are most predictive of productive collaborations. This study addresses this gap by conducting a systematic literature review of 163 studies published since 2000. Grounded in the theoretical framework of multimodal collaboration analytics (MMCA; Schneider et al., 2022), we examine how different data modalities—verbal, gaze, body, head, log, and physiological—are used to assess collaboration. Using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework (Liberati et al., 2009), we categorize studies on the basis of the types of collaborative indicators, the metrics extracted from multimodal data, and the methods used to establish relationships between them. We find several gaps, including an over-representation of lab-based studies with small sample sizes, reliance on simplistic individual or group synchrony metrics, and a lack of standard indicators for collaboration. We discuss related Grand Challenges for MMCA, including scaling up research through field-based studies, developing interpretable models that contribute to theory, computing sophisticated sensor-based metrics that better capture the temporal dynamics of interaction, and designing interventions that support collaboration using fine-grained, high frequency sensor data.</p>

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A quarter century of advances in collaboration sensing: a comprehensive review of high-frequency metrics and their connection to collaborative constructs from 2000 to 2025

  • 期刊论文

  • 发表日期：

    2025年11月22日

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  • Bertrand Schneider,
  • Xiao Fang,
  • Shinyi Kang,
  • Pankaj Chejara,
  • Jiajun Bu

摘要

The increasing availability of multimodal sensing technologies has opened new avenues for studying human interactions. However, there remains a lack of systematic synthesis regarding which multimodal metrics are most predictive of productive collaborations. This study addresses this gap by conducting a systematic literature review of 163 studies published since 2000. Grounded in the theoretical framework of multimodal collaboration analytics (MMCA; Schneider et al., 2022), we examine how different data modalities—verbal, gaze, body, head, log, and physiological—are used to assess collaboration. Using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework (Liberati et al., 2009), we categorize studies on the basis of the types of collaborative indicators, the metrics extracted from multimodal data, and the methods used to establish relationships between them. We find several gaps, including an over-representation of lab-based studies with small sample sizes, reliance on simplistic individual or group synchrony metrics, and a lack of standard indicators for collaboration. We discuss related Grand Challenges for MMCA, including scaling up research through field-based studies, developing interpretable models that contribute to theory, computing sophisticated sensor-based metrics that better capture the temporal dynamics of interaction, and designing interventions that support collaboration using fine-grained, high frequency sensor data.