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arXiv 2609.25414cs.HC

超越任务性能:从评估一种探索性VR交互技术中获得的经验教训

Beyond Task Performance: Lessons Learned from Evaluating an Exploratory VR Interaction Technique

Nevzat Umut Demirseren, Corey Pittman, Isayas Berhe Adhanom, Karthikeyan Umapathy, Kevin Pfeil

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中文总结 AI 辅助

本研究通过案例研究RodCast,比较其与Go-Go Hand和FlowerCone在密集虚拟环境中的交互性能,发现控制简单性至关重要,并强调定性反馈在评估中的必要性。

中文摘要 AI 辅助

密集的虚拟环境对物体选择与操作提出了重大挑战,推动了新型交互技术的发展。本文将RodCast作为一个探索性案例研究,以调查密集虚拟环境中交互的显式轨迹可视化。我们进行了一项受试者内用户研究,将RodCast与Go-Go Hand和FlowerCone在三个代表性交互任务中进行比较,采用客观性能指标、主观评价和定性反馈。结果显示,所提出的实现在更具挑战性的操作任务中产生了性能成本,而定性反馈表明参与者将这些困难归因于双手控制方案的复杂性。此外,主观评价和定性反馈指出了空间意识和目标可达性方面的优势,这些优势并未被传统性能指标充分反映。综合来看,这些发现强调了控制简单性在交互技术设计中的重要性,并强调将定性反馈纳入评估过程对于区分实现局限性与交互概念潜力至关重要。总的来说,这些经验教训为未来交互技术的设计和评估提供了指导。

英文摘要

Dense virtual environments present significant challenges for object selection and manipulation, motivating the development of novel interaction techniques. This paper presents RodCast as an exploratory case study to investigate explicit trajectory visualization for interaction in dense virtual environments. We conducted a within-subjects user study comparing RodCast with Go-Go Hand and FlowerCone across three representative interaction tasks using objective performance measures, subjective evaluations, and qualitative feedback. The results revealed that the proposed implementation incurred performance costs on more demanding manipulation tasks, while qualitative feedback suggested that participants attributed these challenges to the complexity of the bimanual control scheme. Additionally, subjective evaluations and qualitative feedback identified benefits in spatial awareness and target accessibility that were not fully reflected by conventional performance measures. Together, these findings highlight the importance of control simplicity in interaction technique design and emphasize that incorporating qualitative feedback into the evaluation process is essential for distinguishing implementation limitations from the potential of the interaction concept. Collectively, these lessons provide guidance for the design and evaluation of future interaction techniques.

发表机构

  • Texas State University(德克萨斯州立大学)
  • University of Central Florida(中佛罗里达大学)
  • University of North Florida(北佛罗里达大学)

机构由 AI 辅助整理,请以论文原文为准。

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