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Numerous computer-based collaborative learning environments have been developed to support collaborative problem-solving. Yet, understanding the complexity and dynamic nature of the collaboration process remains a challenge. This is particularly true in open-ended immersive learning environments, where students navigate both physical and virtual spaces, pursuing diverse paths to solve problems. In response, we aimed to unpack these complex collaborative learning processes by investigating 16 groups of college students (n = 77) who utilized an immersive astronomy simulation in their introductory astronomy course. Our specific focus is on joint attention as a multi-level indicator to index collaboration. To examine the interplay between joint attention and other multimodal traces (conceptual discussions and gestures) in students’ interactions with peers and the simulation, we employed a multi-granular approach. This approach encompasses macro-level correlations, meso-level network trends, and micro-level qualitative insights from vignettes to capture nuances at different levels. Distinct multimodal engagement patterns emerged between low- and high-achieving groups, evolving over time across a series of tasks. Our findings contribute to the understanding of the notion of timely joint attention and emphasize the importance of individual exploration during the early stages of collaborative problem-solving, demonstrating its contribution to productive knowledge coconstruction. This research overall provides valuable insights into the complexities of collaboration dynamics within and beyond digital space. The empirical evidence we present in our study lays a strong foundation for developing instructional designs aimed at fostering productive collaboration in immersive learning environments. 

Multimodal Learning Analytics (MMLA) has emerged as a powerful approach within the computer-supported collaborative learning community, offering nuanced insights into learning processes through diverse data sources. Despite its potential, the prevalent reliance on traditional instruments such as tripod-mounted digital cameras for video capture often results in sub optimal data quality for facial expressions captured, which is crucial for understanding collaborative dynamics. This study introduces an innovative approach to overcome this limitation by employing 360-degree camera technology to capture students' facial features while collaborating in small working groups. A comparative analysis of 1.5 hours of video data from both traditional tripod-mounted digital cameras and 360-degree cameras evaluated the efficacy of these methods in capturing Facial Action Units (AU) and facial keypoints. The use of OpenFace revealed that the 360-degree camera captured high-quality facial features in 33.17\% of frames, significantly outperforming the traditional method's 8.34\%, thereby enhancing reliability in facial feature detection. The findings suggest a pathway for future research to integrate 360-degree camera technology in MMLA. Future research directions involve refining this technology further to improve the detection of affective states in collaborative learning environments, thereby offering a richer understanding of the learning process.