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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. 

Threshold concepts are transformative elements of domain knowledge that enable those who attain them to engage domain tasks in a more sophisticated way. Existing research tends to focus on the identification of threshold concepts within undergraduate curricula as challenging concepts that prevent attainment of subsequent content until mastered. Recently, threshold concepts have likewise become a research focus at the level of doctoral studies. However, such research faces several limitations. First, the generalizability of findings in past research has been limited due to the relatively small numbers of participants in available studies. Second, it is not clear which specific skills are contingent upon mastery of identified threshold concepts, making it difficult to identify appropriate times for possible intervention. Third, threshold concepts observed across disciplines may or may not mask important nuances that apply within specific disciplinary contexts. The current study therefore employs a novel Bayesian knowledge tracing (BKT) approach to identify possible threshold concepts using a large data set from the biological sciences. Using rubric-scored samples of doctoral students’ sole-authored scholarly writing, we apply BKT as a strategy to identify potential threshold concepts by examining the ability of performance scores for specific research skills to predict score gains on other research skills. Findings demonstrate the effectiveness of this strategy, as well as convergence between results of the current study and more conventional, qualitative results identifying threshold concepts at the doctoral level. 

Purpose This study aims to examine how science, technology, engineering, and mathematics doctoral students interact with postdocs within the research laboratory, identifying the nature and potential impacts of student–postdoc mentoring relationships. Design/methodology/approach Using a sample of 53 doctoral students in the biological sciences, this study uses a sequential mixed-methods design. More specifically, a phenomenological approach enabled the authors to identify how doctoral students make meaning of their interactions with postdocs and other research staff. Descriptive statistics are used to examine how emergent themes might differ as a product of gender and race/ethnicity and the extent to which emergent themes may relate to key doctoral student socialization outcomes. Findings This study reveals six emergent themes, which primarily focus on how doctoral students receive instrumental and psychosocial support from postdocs in their labs. The most frequent emergent theme captures the unique ways in which postdocs provide ongoing, hands-on support and troubleshooting at the lab bench. When examining how this theme plays a role in socialization outcomes, the results suggest that doctoral students who described this type of support from postdocs had more positive mental health outcomes than those who did not describe this type of hands-on support. Originality/value Literature on graduate student mentorship has focused primarily on the impact of advisors, despite recent empirical evidence of a “cascading mentorship” model, in which senior students and staff also play a key mentoring role. This study provides new insights into the unique mentoring role of postdocs, focusing on the nature and potential impacts of student–postdoc interactions.