Do you want to publish a course? Click here

Exploring the Effectiveness of Face-to-face Mixed Reality for Teaching with Chalktalk

62   0   0.0 ( 0 )
 Added by Zhenyi He
 Publication date 2019
and research's language is English




Ask ChatGPT about the research

Teaching that uses projected presentation media such as slide-shows lacks support for dynamic content whose form and behaviors require live changes during a lecture. Recent software alternatives such as the Chalktalk software platform allow the creation of interactive simulations in arbitrary sequences and combinations within presentations. These more dynamic solutions, however, do not optimize for face-to-face interactions: eye-contact, gaze direction, and concurrent awareness of another persons movements together with the presented content. To explore the extent to which these face-to-face interactions may improve learning and engagement during a lecture, we propose a Mixed Reality (MR) platform that places Chalktalks behaviors and simulations within a mirrored virtual world environment designed for face-to-face, one-on-one interactions. We compare our system with projected Chalktalk to evaluate its relative effectiveness for learning, retention, and level of engagement.



rate research

Read More

In recent years, there has been an increasing interest in the use of robotic technology at home. A number of service robots appeared on the market, supporting customers in the execution of everyday tasks. Roughly at the same time, consumer level robots started to be used also as toys or gaming companions. However, gaming possibilities provided by current off-the-shelf robotic products are generally quite limited, and this fact makes them quickly loose their attractiveness. A way that has been proven capable to boost robotic gaming and related devices consists in creating playful experiences in which physical and digital elements are combined together using Mixed Reality technologies. However, these games differ significantly from digital- or physical only experiences, and new design principles are required to support developers in their creative work. This papers addresses such need, by drafting a set of guidelines which summarize developments carried out by the research community and their findings.
Virtual Reality (VR) enables users to collaborate while exploring scenarios not realizable in the physical world. We propose CollabVR, a distributed multi-user collaboration environment, to explore how digital content improves expression and understanding of ideas among groups. To achieve this, we designed and examined three possible configurations for participants and shared manipulable objects. In configuration (1), participants stand side-by-side. In (2), participants are positioned across from each other, mirrored face-to-face. In (3), called eyes-free, participants stand side-by-side looking at a shared display, and draw upon a horizontal surface. We also explored a telepathy mode, in which participants could see from each others point of view. We implemented 3DSketch visual objects for participants to manipulate and move between virtual content boards in the environment. To evaluate the system, we conducted a study in which four people at a time used each of the three configurations to cooperate and communicate ideas with each other. We have provided experimental results and interview responses.
With the popularity of online access in virtual reality (VR) devices, it will become important to investigate exclusive and interactive CAPTCHA (Completely Automated Public Turing test to tell Computers and Humans Apart) designs for VR devices. In this paper, we first present four traditional two-dimensional (2D) CAPTCHAs (i.e., text-based, image-rotated, image-puzzled, and image-selected CAPTCHAs) in VR. Then, based on the three-dimensional (3D) interaction characteristics of VR devices, we propose two vrCAPTCHA design prototypes (i.e., task-driven and bodily motion-based CAPTCHAs). We conducted a user study with six participants for exploring the feasibility of our two vrCAPTCHAs and traditional CAPTCHAs in VR. We believe that our two vrCAPTCHAs can be an inspiration for the further design of CAPTCHAs in VR.
With the mounting global interest for optical see-through head-mounted displays (OST-HMDs) across medical, industrial and entertainment settings, many systems with different capabilities are rapidly entering the market. Despite such variety, they all require display calibration to create a proper mixed reality environment. With the aid of tracking systems, it is possible to register rendered graphics with tracked objects in the real world. We propose a calibration procedure to properly align the coordinate system of a 3D virtual scene that the user sees with that of the tracker. Our method takes a blackbox approach towards the HMD calibration, where the trackers data is its input and the 3D coordinates of a virtual object in the observers eye is the output; the objective is thus to find the 3D projection that aligns the virtual content with its real counterpart. In addition, a faster and more intuitive version of this calibration is introduced in which the user simultaneously aligns multiple points of a single virtual 3D object with its real counterpart; this reduces the number of required repetitions in the alignment from 20 to only 4, which leads to a much easier calibration task for the user. In this paper, both internal (HMD camera) and external tracking systems are studied. We perform experiments with Microsoft HoloLens, taking advantage of its self localization and spatial mapping capabilities to eliminate the requirement for line of sight from the HMD to the object or external tracker. The experimental results indicate an accuracy of up to 4 mm in the average reprojection error based on two separate evaluation methods. We further perform experiments with the internal tracking on the Epson Moverio BT-300 to demonstrate that the method can provide similar results with other HMDs.
Can faces acquired by low-cost depth sensors be useful to catch some characteristic details of the face? Typically the answer is no. However, new deep architectures can generate RGB images from data acquired in a different modality, such as depth data. In this paper, we propose a new textit{Deterministic Conditional GAN}, trained on annotated RGB-D face datasets, effective for a face-to-face translation from depth to RGB. Although the network cannot reconstruct the exact somatic features for unknown individual faces, it is capable to reconstruct plausible faces; their appearance is accurate enough to be used in many pattern recognition tasks. In fact, we test the network capability to hallucinate with some textit{Perceptual Probes}, as for instance face aspect classification or landmark detection. Depth face can be used in spite of the correspondent RGB images, that often are not available due to difficult luminance conditions. Experimental results are very promising and are as far as better than previously proposed approaches: this domain translation can constitute a new way to exploit depth data in new future applications.
comments
Fetching comments Fetching comments
mircosoft-partner

هل ترغب بارسال اشعارات عن اخر التحديثات في شمرا-اكاديميا