Dynamically adjusting the content of augmented reality (AR) applications to efficiently display information best fitting the available screen estate may be important for user performance and satisfaction. Currently, there is not a common practice for dynamically adjusting the content of AR applications based on their apparent size in the user's view of the surround environment. We present a Level-of-Detail AR mechanism to improve the usability of AR applications at any relative size. Our mechanism dynamically renders textual and interactable content based on its legibility, interactability, and viewability respectively. When tested, Level-of-Detail AR functioned as intended out-of-the-box on 44 of the 45 standard user interface Unity prefabs in Microsoft's Mixed Reality Tool Kit. We additionally evaluated impact on task performance, user distance, and subjective satisfaction through a mixed-design user study with 45 participants. Statistical analysis of our results revealed significant task-dependent differences in user performance between the modes. User satisfaction was consistently higher for the Level-of-Detail AR condition.

Current augmented reality (AR) interfaces are often designed for interacting with one application at a time, significantly limiting a user’s ability to concurrently interact with and switch between multiple applications or modalities that could run in parallel. In this work, we introduce an application model called Layerable Apps, which supports a variety of AR application types while enabling multitasking through concurrent execution, fast application switching, and the ability to layer application views to adjust the degree of augmentation to the user’s preference. We evaluated Layerable Apps through a within-subjects user study (n=44), compared against a traditional single-focus application model on a split-information task involving the simultaneous use of multiple applications. We report the results of our study, where we found differences in quantitative task performance, favoring Layerable mode. We also analyzed app usage patterns, spatial awareness, and overall preferences between both modes as well as between experienced and novice AR users.

Brain Computer Interfaces (BCIs) typically utilize electroencephalography (EEG) to enable control of a computer through brain signals. However, EEG is susceptible to a large amount of noise, especially from muscle activity, making it difficult to use in ubiquitous computing environments where mobility and physicality are important features. In this work, we present a novel multimodal approach for classifying the P300 event related potential (ERP) component by coupling EEG signals with nonscalp electrodes (NSE) that measure ocular and muscle artifacts. We demonstrate the effectiveness of our approach on a new dataset where the P300 signal was evoked with participants on a stationary bike under three conditions of physical activity: rest, low-intensity, and high-intensity exercise. We show that intensity of physical activity impacts the performance of both our proposed model and existing state-of-the-art models. After incorporating signals from nonscalp electrodes our proposed model performs significantly better for the physical activity conditions. Our results suggest that the incorporation of additional modalities related to eye-movements and muscle activity may improve the efficacy of mobile EEG-based BCI systems, creating the potential for ubiquitous BCI.

We propose an Augmented Reality vocabulary learning interface in which objects in a user's environment are automatically recognized and labeled in a foreign language. Using AR for language learning in this manner is still impractical for a number of reasons. Scalable object recognition and consistent labeling of objects is still a significant challenge, and interaction with arbitrary physical objects in AR scenes has consequently not been well explored. To help address these challenges, we present a system that utilizes real-time object recognition to perform semantic labeling and object registration in Augmented Reality. We discuss its implementation, our motivations in designing it, and how it can be applied to AR language learning applications.

Augmented Reality is a promising interaction paradigm for learning applications. It has the potential to improve learning outcomes by merging educational content with spatial cues and semantically relevant objects within a learner's everyday environment. The impact of such an interface could be comparable to the method of loci, a well known memory enhancement technique used by memory champions and polyglots. However, using Augmented Reality in this manner is still impractical for a number of reasons. Scalable object recognition and consistent labeling of objects is a significant challenge, and interaction with arbitrary (unmodeled) physical objects in AR scenes has consequently not been well explored. To help address these challenges, we present a framework for in-situ object labeling and selection in Augmented Reality, with a particular focus on language learning applications. Our framework uses a generalized object recognition model to identify objects in the world in real time, integrates eye tracking to facilitate selection and interaction within the interface, and incorporates a personalized learning model that dynamically adapts to student's growth. We show our current progress in the development of this system, including preliminary tests and benchmarks. We explore challenges with using such a system in practice, and discuss our vision for the future of AR language learning applications.

We conducted a fundamental user study to assess potential benefits of AR technology for immersive vocabulary learning. With the idea that AR systems will soon be able to label real-world objects in any language in real time, our within-subjects lab-based study explores the effect of such an AR vocabulary prompter on participants learning nouns in an unfamiliar foreign language, compared to a traditional flashcard-based learning approach. Our results show that the immersive AR experience of learning with virtual labels on real-world objects is both more effective and more enjoyable for the majority of participants, compared to flashcards. Specifically, when participants learned through augmented reality, they scored significantly better on both same-day and 4-day delayed productive recall tests than when they learned using the flashcard method. We believe this result is an indication of the strong potential for language learning in augmented reality, particularly because of the improvement shown in sustained recall compared to the traditional approach.

Massive open online courses (MOOCs) have high potential for improving education worldwide, but understanding of student behavior and situations is difficult to achieve in online settings. Network analytics and visualizations can assist instructors with supporting understanding of student behavior as courses unfold. In this work, we perform a visual comparative analysis of two different MOOC courses to analyze the impacts of course structure differences and demonstrate the benefits of visual network analysis in this context. We present several insights: (1) behavior features that are best for prediction of student attrition varied with course structure, (2) a large proportion (about 35%) of students never received a reply to their original post and this was correlated with an eventual dropout, and (3) students that received a reply to their original post were twice as likely to post again. We contribute several information visualizations of student network data and draw recommendations for MOOC instructors and designers of course systems.