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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. 

Extensive research underscores the importance of stimulating students' interest in learning, as it can improve key educational outcomes such as self-regulation, collaboration, problem-solving, and overall enjoyment. Yet, the mechanisms through which interest manifests and impacts learning remain less explored, particularly in open-ended game-based learning environments like Minecraft. The unstructured nature of gameplay data in such settings poses analytical challenges. This study employed advanced data mining techniques, including changepoint detection and clustering, to extract meaningful patterns from students' movement data. Changepoint detection allows us to pinpoint significant shifts in behavior and segment unstructured gameplay data into distinct phases characterized by unique movement patterns. This research goes beyond traditional session-level analysis, offering a dynamic view of the learning process as it captures changes in student behaviors while they navigate challenges and interact with the environment. Three distinct exploration patterns emerged: surface-level exploration, in-depth exploration, and dynamic exploration. Notably, we found a negative correlation between surface-level exploration and interest development, whereas dynamic exploration positively correlated with interest development, regardless of initial interest levels. In addition to providing insights into how interest can manifest in Minecraft gameplay behavior, this paper makes significant methodological contributions by showcasing innovative approaches for extracting meaningful patterns from unstructured behavioral data within game-based learning environments. The implications of our research extend beyond Minecraft, offering valuable insights into the applications of changepoint detection in educational research to investigate student behavior in open-ended and complex learning settings. 

With the increasing adoption of collaborative learning approaches, instructors must understand students’ problem-solving approaches during collaborative activities to better design their class. Among the multiple ways to reveal collaborative problem-solving processes, temporal submission patterns is one that is more scalable and generalizable in Computer Science education. In this paper, we provide a temporal analysis of a large dataset of students’ submissions to collaborative learning assignments in an upper-level database course offered at a large public university. The log data was collected from an online assessment and learning system, containing the timestamps of each student’s submissions to a problem on the collaborative assignment. Each submission was labeled as quick (Q), medium (M), or slow (S) based on its duration and whether it was shorter or longer than the 25th and 75th percentile. Sequential compacting and mining techniques were employed to identify pairs of transitions highly associated with one another. This preliminary research sheds light on the recurring submission patterns derived from the amount of time spent on each problem, warranting further examination on these patterns to unpack collaborative problem-solving behaviors. Our study demonstrates the potential of temporal analysis to identify meaningful problem-solving patterns based on log traces, which may help flag key moments and alert instructors to provide in-time scaffolding when students work on group assignments.