Effect of Collaboration Mode and Position Arrangement on Immersive Analytics Tasks in Virtual Reality: A Pilot Study

[Background] Virtual reality (VR) technology can provide unique immersive experiences for group users, and especially for analytics tasks with visual information in learning. Providing a shared control/view may improve the task performance and enhance the user experience during VR collaboration. [Objectives] Therefore, this research explores the effect of collaborative modes and user position arrangements on task performance, user engagement, and collaboration behaviors and patterns in a VR learning environment that supports immersive collaborative tasks. [Method] The study involved two collaborative modes (shared and non-shared view and control) and three position arrangements (side-by-side, corner-to-corner, and back-to-back). A user study was conducted with 30 participants divided into three groups (Single, Shared, and Non-Shared) using a VR application that allowed users to explore the structural and transformational properties of 3D geometric shapes. [Results] The results showed that the shared mode would lead to higher task performance than single users for learning analytics tasks in VR. Besides, the side-by-side position got a higher score and more favor for enhancing the collaborative experience. [Conclusion] The shared view would be more suitable for improving task performance in collaborative VR. In addition, the side-by-side position may provide a higher user experience when collaborating in learning VR. From these results, a set of guidelines for the design of collaborative visualizations for VR environments are distilled and presented at the end of the paper. All in all, although our experiment is based on a colocated setting with two users, the results are applicable to both colocated and distributed collaborative scenarios with two or more users.

Decision-Maker-Oriented VS. Collaboration: China’s Public Participation in Environmental Decision-Making

Public participation in environmental decision-making (EDM) has been broadly discussed. However, few recent studies in English have focused on the participation subject, scope, ways, and procedure in the EDM of developing countries such as China in the worldwide governance transformation. This study aims to provide an overview of public participation in EDM in China, thus elucidating both the legislation and practice of public participation in EDM in China to a broader audience, as such an overview has not yet been provided. At the beginning of this article, we clarify the key definitions of EDM, public participation and the public, and establish an analytical framework for analyzing public participation in EDM in China. We analyze the scope of the public, the participation scope, ways of participating, and participation procedure in EDM in legislation and practice, through document analysis and empirical survey. We then comment on challenges for public participation in EDM in China—including low public participation in EDM, narrow scope of participation, unbalanced ways of participation, and unreasonable participation procedure. In conclusion, we draw wider implications for public participation in EDM in China, arguing for a transformation from a decision-maker-oriented mode to a collaboration mode.

Mass Collaboration-Driven Method for Recommending Product Ideas Based on Dempster-Shafer Theory of Evidence

In the mass collaboration mode, there exist a large number of product ideas with low value density and thousands of participants who are differed on their professional backgrounds, knowledge structures, and value orientations. It is impossible for each participant to give a comprehensive evaluation of each idea as that in traditional methods for the reasons as mentioned above. In order to solve this problem, a mass collaboration-driven method for recommending product ideas is proposed based on Dempster-Shafer theory of evidence (DST). Firstly, the method for computing basic probability assignment (BPA) function, which can effectively reflect the facticity of experts’ evaluations, is introduced by discounting belief degrees with weights to extract the evaluation information of product ideas. Then, Dempster’s combination rule is used to combine the derived BPA functions for two times: the first one is to combine the discounted BPA functions on all criteria with respect to a specified expert and the other is to combine the combined BPA functions for all experts with respect to a specified alternative. Finally, the steps of mass collaboration-driven method for recommending product ideas based on the DST are proposed. An illustrative example is provided to demonstrate the applicability of the proposed method.