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To enhance learning and training, VR haptic devices must provide safe and physically accurate feedback that helps users build intuitions about force and motion. We present TrueForce, a method for rendering force feedback by capturing user input and reconfiguring a compliant mechanism in real time to align perceived with expected forces. We implemented TrueForce in a grounded Encountered-Type Haptic Display (ETHD) and demonstrated its versatility through three representative physics scenarios: pushing a stationary object, experiencing electrostatic repulsion, and bouncing a moving object. A user study with 29 participants showed that users adapted to new interaction profiles within 2–3 trials, and that rendered forces remained within perceptual thresholds except in the heaviest and most dynamic conditions. These results suggest that users can rapidly adapt to the system’s force-motion mappings, providing the consistent sensorimotor feedback necessary for building physical intuitions when interacting with moving virtual objects. 

Not AvailableExtended Reality (XR) learning environments generate rich behavioral data through embodied interaction—head movements, gaze patterns, and spatial navigation—that could enable passive assessment without interrupting the learning experience. We investigate spatial exploration behavior in ACHIEVE, an XR environment for neural network visualization learning. In a between-subjects study (N = 56), XR participants exhibited significantly different spatial behavior than desktop users: over 14 times greater pointer movement (M = 132.5 m vs. M = 9.5 m), extensive head rotation (M = 9,818◦ ), and M = 22.5 m of head translation during the learning session. We visualize individual exploration patterns through head and pointer trajectory traces, revealing substantial variation in how learners navigate the 3D content. These spatial metrics, automatically captured during learning, represent a promising avenue for passive assessment of embodied engagement—enabling educators to identify struggling learners, provide personalized feedback, and adapt content delivery without intrusive testing. Full learning outcomes and user experience metrics are reported in companion publications; here we focus on spatial behavior as a novel contribution toward spatialized learning analytics in education 

Immersive virtual reality (VR) experiences require transmission and rendering of large-scale 3D content, often represented as point clouds or polygon meshes. Unfortunately, existing networked VR systems often fail to fully exploit the flexibility of VR data representations. To address this problem, we propose a cross-layer design that elevates a network data unit to a usable rendering unit for VR applications. Our aim is to bridge the gap between networks and applications in order to enhance visual quality, especially over constrained and variable networks. Our approach, Rendering Unit that is Network-aware (RUN), with two variants, RUN-Packet and RUN-Hybrid, includes mechanisms to effectively utilize network data units when encoding, transmitting, decoding, and rendering. Specifically, we develop additive detail refinement mechanisms and address streaming challenges such as head-of-line (HoL) blocking. We prototype our system in Unity 3D and evaluate it using synthetic network environments and real network traces. Our results with both static and dynamic point clouds demonstrate that RUN significantly reduces stalls and delivers smoother frame updates, enhancing visual quality.