Augmented-Virtual Reality: How to Improve Education Systems

This essay presents and discusses the developing role of virtual and augmented reality technologies in education. Addressing the challenges in adapting such technologies to focus on improving students’ learning outcomes, the author discusses the inclusion of experiential modes as a vehicle for improving students’ knowledge acquisition. Stakeholders in the educational role of technology include students, faculty members, institutions, and manufacturers. While the benefits of such technologies are still under investigation, the technology landscape offers opportunities to enhance face-to-face and online teaching, including contributions in the understanding of abstract concepts and training in real environments and situations. Barriers to technology use involve limited adoption of augmented and virtual reality technologies, and, more directly, necessary training of teachers in using such technologies within meaningful educational contexts. The author proposes a six-step methodology to aid adoption of these technologies as basic elements within the regular education: training teachers; developing conceptual prototypes; teamwork involving the teacher, a technical programmer, and an educational architect; and producing the experience, which then provides results in the subsequent two phases wherein teachers are trained to apply augmented- and virtual-reality solutions within their teaching methodology using an available subject-specific experience and then finally implementing the use of the experience in a regular subject with students. The essay concludes with discussion of the business opportunities facing virtual reality in face-to-face education as well as augmented and virtual reality in online education

TPICDS: A Two-Phase Parallel Approach for Incremental Clustering of Data Streams

Parallel and distributed solutions are essential for clustering data streams due to the large volumes of data. This paper first examines a direct adaptation of a recently developed prototype-based algorithm into three existing parallel frameworks. Based on the evaluation of performance, the paper then presents a customised pipeline framework that combines incremental and twophase learning into a balanced approach that dynamically allocates the available processing resources. This new framework is evaluated on a collection of synthetic datasets. The experimental results reveal that the framework not only produces correct final clusters on the one hand, but also significantly improves the clustering efficienc

Towards Big Data Visualization for Augmented Reality

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