This study compared how students engage in inductive and deductive geometric reasoning using augmented reality (AR) by analyzing their geometric thinking elements, gestures, and cognitive functions of actions while collaboratively producing geometric proofs. High school students (N=14; seven dyads) completed inductive (pyramid) and deductive (cylinder) tasks using AR. Students’ speech and actions were coded for geometric thinking elements (operational, logical, and generalizable), action types (pointing, depictive gestures), and cognitive functions (pragmatic, epistemic, complementary), then analyzed using Epistemic Network Analysis (ENA). Results revealed distinct reasoning patterns: inductive reasoning relied more on external resources through pragmatic actions and pointing gestures to generate insights and form logical and generalizable thought, while deductive reasoning involved complementary actions and dynamic depictive gestures that externalized mental processes to construct logical and operational thought. These findings illustrate how AR interactions differentially support inductive and deductive geometric reasoning, informing the design of AR-based learning experiences.

This paper introduces a novel beam tracking scheme for full-duplex ground user terminals aiming to transmit uplink and receive downlink from two low Earth orbit (LEO) satellites at the same time and same frequency. Our proposed technique leverages phenomena observed in recent measurements to strategically select transmit and receive beams which couple low selfinterference across the satellites' trajectories, thereby enabling in-band full-duplex operation. By taking a measurement-driven approach, our scheme circumvents the need for explicit selfinterference channel estimation and can inherently account for hardware nonidealities. We show that our proposed scheme reliably selects beams which spatially cancels self-interference to below the noise floor, avoiding the need for digital/analog cancellation. Simulation results using satellite and orbital parameters published in 3GPP and FCC filings show that this substantial reduction in self-interference does not prohibitively compromise beamforming gain, allowing user terminals to attain near-maximal SINRs, thus unlocking full-duplex operation.

This paper addresses the problem of multi-risk measure agnostic multi-armed bandits in heavy-tailed reward settings. We propose a framework that leverages novel deviation inequalities for the $1$-Wasserstein distance to construct confidence intervals for Lipschitz risk measures. The distributional LCB (DistLCB) algorithm is introduced, which achieves asymptotic optimality by deriving the first lower bounds for risk measure aware bandits with explicit sub-optimality gap dependencies. The DistLCB is further extended to multi-risk objectives, which enables Pareto-optimal solutions that consider multiple aspects of reward distributions. Additionally, we provide a regret analysis that includes both gap-dependent and gap-independent bounds for multi-risk settings. Experiments validate the effectiveness of the proposed methods in synthetic and real-world applications.

This study explores the role of explanatory narration, delivered as non-diegetic audio—in supporting conceptual understanding and long-term motivation in immersive virtual reality (VR). Building on an open-source VR simulation where users embody an Adélie penguin, we conducted a between-subjects experiment (N = 30) to examine how synchronized narration impacts learning outcomes, embodiment, and post-experience engagement. Participants performed four embodied tasks—waddling, nesting, mating, and predator defense—with or without narration. Assessments included immediate and delayed knowledge tests, a 16-item embodiment questionnaire, and motivation measures three weeks post-experience.

This study investigates an interference coordination for multiple unmanned aerial vehicle (UAV)-enabled communications under a probabilistic Line-of-Sight (LoS) channel model. Given that the LoS probability of a wireless channel is determined by the elevation angle between the UAV and ground node (GN), we jointly optimize the 3-D trajectory, user scheduling, and transmit power of the UAVs to maximize the minimum average spectral efficiency (SE) among GNs. We first present an effective lower bound for the channel-state-dependent SE to handle its complex form that inevitably arises from the probabilistic LoS channel model. To solve the nonconvex optimization problem under consideration, we also divide the original problem into four subproblems and apply a successive convex approximation to make each subproblem convex for a relevant optimization variable. We then propose an iterative algorithm based on a block coordinate descent method to efficiently find the optimal solution for each convex subproblem. The results of extensive simulation show that to mitigate interference with other UAV networks, each UAV optimizes its horizontal and vertical trajectories, along with its radio resources, to establish LoS for desired channels and Non-LoS for interference channels. The proposed scheme is also verified to be superior to conventional schemes in terms of average SE by effectively adjusting co-channel interference between different UAV networks.

This paper considers a multiple unmanned aerial vehicle (UAV)-enabled wireless powered communication network (WPCN). In this WPCN, UAVs broadcast radio frequency (RF) signals to facilitate a wireless power transfer (WPT) during the downlink phase, and ground nodes (GNs) harvest energy from these RF signals and transmit data to their respective UAVs in the uplink phase. To maximize the minimum uplink throughput of GNs, we jointly optimize the scheduling, transmit power of GNs, and trajectory of UAVs, while satisfying the energy neutrality of GNs and the mobility constraints of UAVs. To solve this non-convex optimization problem, we apply a successive convex approximation to divide the original problem into subproblems and make each of them convex for each optimization variable. Subsequently, we propose an iterative algorithm based on a block coordinate descent technique and efficiently find the optimal solution for each convex subproblem. The simulation result reveals that resource allocation and the trajectory of UAVs are strongly influenced by the interference level within the network. Furthermore, the result verifies that the proposed optimization approach significantly outperforms existing baseline schemes by properly coordinating co-channel and cross-link interferences between distinct UAV networks.

This work-in-progress paper was to examine the effect of the interactive conversation in a teaching simulation for classroom management training. We developed SimTEACHER, a teaching simulation, and the scenario aims to manage the classroom during collaborative learning. Twenty-five in-service secondary school teachers participated in this study and experienced both observation and interactive conversation in the simulation. They evaluated perceptions of problem behavior, scenario usefulness, and situational interest on each simulation. The result showed no difference in perception of problem behavior in each simulation. The scenario usefulness and situational interest were significantly higher in the interactive conversation.