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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. 

Self-regulated learning (SRL) is the ability to regulate cognitive, metacognitive, motivational, and emotional states while learning and is posited to be a strong predictor of academic success. It is therefore important to provide learners with effective instructions to promote more meaningful and effective SRL processes. One way to implement SRL instructions is through providing real-time SRL scaffolding while learners engage with a task. However, previous studies have tended to focus on fixed scaffolding rather than adaptive scaffolding that is tailored to student actions. Studies that have investigated adaptive scaffolding have not adequately distinguished between the effects of adaptive and fixed scaffolding compared to a control condition. Moreover, previous studies have tended to investigate the effects of scaffolding at the task level rather than shorter time segments—obscuring the impact of individual scaffolds on SRL processes. To address these gaps, we (a) collected trace data about student activities while working on a multi-source writing task and (b) analyzed these data using a cutting-edge learning analytic technique— ordered network analysis (ONA)—to model, visualize, and explain how learners' SRL processes changed in relation to the scaffolds. At the task level, our results suggest that learners who received adaptive scaffolding have significantly different patterns of SRL processes compared to the fixed scaffolding and control conditions. While not significantly different, our results at the task segment level suggest that adaptive scaffolding is associated with earlier engagement in SRL processes. At both the task level and task segment level, those who received adaptive scaffolding, compared to the other conditions, exhibited more task-guided learning processes such as referring to task instructions and rubrics in relation to their reading and writing. This study not only deepens our understanding of the effects of scaffolding at different levels of analysis but also demonstrates the use of a contemporary learning analytic technique for evaluating the effects of different kinds of scaffolding on learners' SRL processes.

 

Collaborative robots, or cobots, represent a breakthrough technology designed for high-level (e.g., collaborative) interactions between workers and robots with capabilities for flexible deployment in industries such as manufacturing. Understanding how workers and companies use and integrate cobots is important to inform the future design of cobot systems and educational technologies that facilitate effective worker-cobot interaction. Yet, little is known about typical training for collaboration and the application of cobots in manufacturing. To close this gap, we interviewed nine experts in manufacturing about their experience with cobots. Our thematic analysis revealed that, contrary to the envisioned use, experts described most cobot applications as only low-level (e.g., pressing start/stop buttons) interactions with little flexible deployment, and experts felt traditional robotics skills were needed for collaborative and flexible interaction with cobots. We conclude with design recommendations for improved future robots, including programming and interface designs, and educational technologies to support collaborative use.