## 1 Introduction  
In today's rapidly digitizing work environment, effective team collaboration is crucial for organizational success [1]. The shift towards virtual interactions, accelerated by the COVID-19 pandemic, has transformed occasional virtual meetings into daily necessities [2]. This transition underscores the need for research into the complexities of team dynamics and performance in virtual settings [3, 4]. Identifying key drivers of productivity in these environments is increasingly critical [5].  
Historically, research on team collaboration and performance has relied on subjective measures, such as surveys and questionnaires [6]. While valuable, these methods often fail to capture the real-time complexities of team interactions and are susceptible to biases [7]. This highlights the need for objective, real-time measures of team collaboration and performance [8], emphasizing data-driven approaches to understanding team dynamics.  
Teamwork is critical in various settings, including schools, universities, and organizations, where collective efforts often yield superior outcomes compared to individual contributions [9, 10]. Effective teamwork hinges on clear goals, trust, role allocation, and conflict management [11]. Research has explored how teams form, develop, and achieve their objectives [12]. Understanding teamwork processes is essential for grasping how teams' function and achieve goals. These processes convert inputs into outcomes through cognitive, verbal, and behavioral activities [13]. Team processes are dynamic, adapting to task demands and environmental complexities, which influence team performance and success [14].  
The effectiveness of teams focuses on efficiency, adaptability, and productivity within groups striving towards common goals [15]. Several models explain team effectiveness, including [PERSON]'s Input-Process-Output (IPO) model, which systematizes teamwork into sequential stages: inputs, processes, and outputs [16]. The Input-Mediator-Output-Input (IMOI) model enhances the IPO model by recognizing the dynamic nature of teams through continuous feedback [17]. The Multi-team Systems (MTS) framework explores interactions among multiple teams within broader organizational structures, which is crucial for large projects [18].  
The real-time feedback provided to the treatment group consisted of three components displayed on a dashboard: the Happiness Indicator (a gauge showing the average happiness level of participants), the Peak Indicator (a gauge reflecting the variance of emotions over time), and the Groupflow Indicator (a gauge measuring the synchronicity of the group's emotions). Future work will delve into which of these components are most impactful. However, based on a decade of research on virtual mirroring, we anticipate that the mere presence of feedback, regardless of its specific form, will likely lead to improved performance due to the Hawthorne effect [19].  
The remainder of this paper is structured as follows: Section 2 provides the background and motivation for this research, delving into the theoretical foundations of virtual teamwork, the role of emotions and communication, and the significance of real-time feedback. Section 3 details the materials and methods employed in our study, including the experimental design, data collection procedures, and the measures used to assess team performance and dynamics. Section 4 presents the results of our analysis, highlighting key findings related to the impact of real-time emotional feedback on virtual team performance and the predictive power of multi-modal data. Section 5 discusses the implications of our findings, addressing the research questions and hypotheses, and exploring the broader implications for virtual team collaboration and the development of effective collaboration tools. Finally, Section 6 concludes the paper by summarizing the key takeaways and outlining potential avenues for future research.