Assessment of QoE and Learning Effectiveness in Collaborative Mixed Reality Environments

• Main Author: Vellingiri, Shanthi
• Format: Dissertation
• Language: English
• Published: ProQuest Dissertations & Theses 01.01.2020
• Subjects: Active Learning; Algorithms; Architecture; Computer science; Computer Simulation; Educational technology; Engineering; Inferences; Instructional Design; Instructional Effectiveness; Mathematical Models; Networks; Travel; Visualization
• ISBN: 9798460405329

Be it a safety-critical or a luxury application, Mixed Reality (MR) assists in virtual education to travel by reproducing a real-world experience to offer flexibility and convenience. Collaborative Mixed Reality Environments involve the participation of multiple users, spatial, time constraints, and several interaction modalities for completing a task. Providing a better Quality of Experience (QoE) is essential for user engagement and satisfaction. Maintaining as-natural-as-possible interactions, multi-modal content to enhance usability and body ownership, 360-degree immersive experience, virtual assistance through instructional design, involving simultaneous physical and virtual colocation of users improve users QoE. One solution fits all is not applicable for collaborative mixed reality environments, considering the abundance of techniques ranging from interaction, content creation, visualization, to rendering. However, providing (and maintaining) an interactive environment with an appropriate collaboration type based on the interaction modality will ensure system and interaction fidelity, immersion, responsiveness, presence, copresence, and interactivity. Therefore, novel approaches to maintain the above characteristics estimating network influence provide a better QoE and learning effectiveness in collaborative mixed reality environments. This dissertation focuses on collaborative mixed reality environments, presenting a brief survey on factors influencing QoE, it addresses the need for an objective assessment and the design of a multi-faceted framework. Primarily centering on manipulation and exploration tasks with a coequal or leader-follower type of collaboration, the dissertation addresses the challenges of the proposed component-based framework with contributions to QoE and learning effectiveness. The foremost challenge is designing these collaborative mixed reality environments as it involves various options on task types (manipulation, conversing, instructing, and exploration), interaction methods (voice, computer-vision, and device-based), nature of collaboration (coequal, leader-follower), and assessment (objective, subjective) approach. Accommodating the above-mentioned characteristics, we designed a authoring framework called SCeVE, it offers a quick design of several collaborating tasks. We identified the design choices or key components of the authoring framework: Synchronization, Collaborative exploration, Visualization, and Evaluation. Offering travel, travel-aid, virtual camera strategies, intercoherency among multimedia modalities, and objective QoE assessment models, the SCeVE framework ease authoring of exploration-based tasks. In collaborative mixed reality environments, networked impairments on the interactions degrade task performance. As interactions involve multiple users, subjective assessment of QoE will be time-consuming and prone to user bias. Therefore, the need for an objective measurement is essential to understand the safe operation boundaries of a collaborative MR application, for the reason that it might aid in the estimation of QoE considering the additional design changes of the system. Identifying a fundamental metric, we modeled an objective Hierarchical Position Discrepancy Model (HPDM) that measures variances in the visual modality. The benefit of the model is that it predicts possible QoE requiring the least user studies. A collaborative application depends on the network for effective operation. To study the network influence on the design choices, we examined the influence of network impairment on Synchronization, Collaborative exploration, and Visualization design choices to an exploration task involving trans-pacific participation. Through a modified Virtual Reality (mVR) triangle strategy, we identified the effect of the network parameters on the three design choices in providing interactivity, presence, and co-presence. The balance of interactivity, presence, and co-presence is necessary to maintain QoE. Finally, we examine the learning effectiveness, collaborative mixed reality environments are known for their potential to offer interactivity, a beneficial characteristic for active learning. The backbone of active learning is practice or activity, considering a self-governing Augmented Virtuality (AV) tour with travel, we proposed a quiz with corrective feedback strategy to evaluate the learning effectiveness through active learning in collaborative mixed reality environments. AV is a subcategory of MR; it merges real-world objects and their actions to a virtual world. Exploring in a field trip on tree taxonomy, 30 student participants from Eastfield College and the University of Texas at Dallas participated. The pre-post study results demonstrate the learning effectiveness through the collaborative mixed reality environment.