## 2 Background and Motivation  
The rise of information and communication technology has transformed traditional teamwork into virtual teamwork, especially post-COVID-19 [20]. Virtual teams collaborate primarily through electronic means, introducing challenges such as the absence of face-to-face interactions and managing cultural diversity [21; 22]. Effective management of non-verbal communication and technological disruptions is essential to maintaining collaboration [23; 24]. Traditional teamwork models also apply to virtual teams. The IPO model, used in virtual team research, focuses on team dynamics and effectiveness in digital settings [25; 26]. The Virtual Team Maturity Model (VTMM) assesses virtual team progression through different maturity levels, identifying key competencies at each stage [27]. Media Richness Theory (MRT) and Social Presence Theory (SPT) emphasize selecting communication tools that effectively convey cues and enhance the sense of presence, essential for virtual teams [28; 29]. The Media Naturalness Theory (MNT) argues that deviations from face-to-face communication increase cognitive effort, impacting communication effectiveness in virtual environments [30]. Assessing teamwork effectiveness has evolved significantly. Historically, observational and subjective measures, such as self-report questionnaires and direct observations were most prominent. Tools like the Teamwork Perception Questionnaire (TPQ) and Team Climate Inventory (TCI) capture individual perceptions of teamwork quality [31; 32]. Objective performance metrics linked teamwork to tangible outcomes like deadlines, error rates, and product quality [33]. Technological advancements have introduced new methods for measuring teamwork effectiveness. Simulation-based assessments present teams with predefined challenges to evaluate performance, communication, and adaptability [34]. Digital analytics leverage data from virtual platforms, analyzing communication patterns and interactions to gauge teamwork dynamics [35]. Recently, the analysis of body signals through computer vision and machine learning has emerged, offering real-time insights into team interactions, engagement, and emotional states [36; 37]. This leads us to explore the potential of predicting virtual team performance through multi-modal data analysis, focusing on emotional states and conversational dynamics. Consequently, we propose our first hypothesis:  
Through an analysis of multi-modal data, it is possible to accurately predict the teamwork performance of virtual teams. Effective team performance is influenced by several key factors. Team composition, including a mix of skills, knowledge, and personalities, is critical, with diverse teams often balancing complementary skills and shared values [38]. Communication frequency and quality, along with robust feedback loops, are essential for effective collaboration [39]. Shared mental models among team members enhance coordination and reduce misunderstandings, leading to better performance [40]. Trust and a sense of belonging significantly contribute to a collaborative environment, enhancing overall team effectiveness [41]. Clear and aligned goals guide team direction and performance [42]. Emotions play a crucial role in team dynamics, influencing communication patterns, decision-making processes, and overall team cohesion [43]. Emotions, moods, and effects are fundamental to understanding team interactions. Emotions are intense, short-lived feelings triggered by specific events, directly impacting team dynamics [44]. Moods are less intense but more enduring states that subtly influence team interactions and perceptions over time [45]. Affect encompasses both emotions and moods, providing a spectrum for analyzing emotional states in teams [46]. Positive emotional states, such as happiness, can improve performance, while balanced conversation dynamics characterized by equitable turn-taking enhance collaboration [47; 48].