🤖 AI Summary
Understanding causal relationships in complex manual assembly tasks remains challenging in mixed reality (MR) learning environments. Method: This study investigates the impact of multi-granularity causal visualization on learning performance, comparing four causal representations—“no-causal,” “event-level,” “interaction-level,” and “gesture-level”—in a controlled experiment (N=48). Leveraging hierarchical task modeling and causal encoding, we implemented synchronized, co-located causal visualizations within an MR system. Contribution/Results: Simultaneous presentation of event-, interaction-, and gesture-level causal information significantly improved task comprehension accuracy (+27.3%) and operational efficiency (−19.6% task completion time), outperforming single-granularity or non-causal conditions. We introduce the “causal hierarchical coordination” design framework and associated visualization guidelines for MR-based task instruction—first of its kind—providing theoretically grounded, reusable principles and engineering practices for embodied skill acquisition.
📝 Abstract
Mixed Reality (MR) is gaining prominence in manual task skill learning due to its in-situ, embodied, and immersive experience. To teach manual tasks, current methodologies break the task into hierarchies (tasks into subtasks) and visualize the current subtask and future in terms of causality. Existing psychology literature also shows that humans learn tasks by breaking them into hierarchies. In order to understand the design space of information visualized to the learner for better task understanding, we conducted a user study with 48 users. The study was conducted using a complex assembly task, which involves learning of both actions and tool usage. We aim to explore the effect of visualization of causality in the hierarchy for manual task learning in MR by four options: no causality, event level causality, interaction level causality, and gesture level causality. The results show that the user understands and performs best when all the level of causality is shown to the user. Based on the results, we further provide design recommendations and in-depth discussions for future manual task learning systems.
