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Augmented/Virtual reality and video-based media play a vital role in the digital learning revolution to train novices in spatial tasks. However, creating content for these different media requires expertise in several fields. We present EditAR, a unified authoring, and editing environment to create content for AR, VR, and video based on a single demonstration. EditAR captures the user’s interaction within an environment and creates a digital twin, enabling users without programming backgrounds to develop content. We conducted formative interviews with both subject and media experts to design the system. The prototype was developed and reviewed by experts. We also performed a user study comparing traditional video creation with 2D video creation from 3D recordings, via a 3D editor, which uses freehand interaction for in-headset editing. Users took 5 times less time to record instructions and preferred EditAR, along with giving significantly higher usability scores. 

Abstract Augmented reality (AR) is a unique, hands-on tool to deliver information. However, its educational value has been mainly demonstrated empirically so far. In this paper, we present a modeling approach to provide users with mastery of a skill, using AR learning content to implement an educational curriculum. We illustrate the potential of this approach by applying this to an important but pervasively misunderstood area of STEM learning, electrical circuitry. Unlike previous cognitive assessment models, we break down the area into microskills—the smallest segmentation of this knowledge—and concrete learning outcomes for each. This model empowers the user to perform a variety of tasks that are conducive to the acquisition of the skill. We also provide a classification of microskills and how to design them in an AR environment. Our results demonstrated that aligning the AR technology to specific learning objectives paves the way for high quality assessment, teaching, and learning. 

The US manufacturing industry is currently facing a welding workforce shortage which is largely due to inadequacy of widespread welding training. To address this challenge, we present a Virtual Reality (VR)-based training system aimed at transforming state-of-the-art-welding simulations and in-person instruction into a widely accessible and engaging platform. We applied backward design principles to design a low-cost welding simulator in the form of modularized units through active consulting with welding training experts. Using a minimum viable prototype, we conducted a user study with 24 novices to test the system’s usability. Our findings show (1) greater effectiveness of the system in transferring skills to real-world environments as compared to accessible video-based alternatives and, (2) the visuo-haptic guidance during virtual welding enhances performance and provides a realistic learning experience to users. Using the solution, we expect inexperienced users to achieve competencies faster and be better prepared to enter actual work environments. 

Augmented reality (AR) is an efficient form of delivering spatial information and has great potential for training workers. However, AR is still not widely used for such scenarios due to the technical skills and expertise required to create interactive AR content. We developed ProcessAR, an AR-based system to develop 2D/3D content that captures subject matter expert’s (SMEs) environment-object interactions in situ. The design space for ProcessAR was identified from formative interviews with AR programming experts and SMEs, alongside a comparative design study with SMEs and novice users. To enable smooth workflows, ProcessAR locates and identifies different tools/objects through computer vision within the workspace when the author looks at them. We explored additional features such as embedding 2D videos with detected objects and user-adaptive triggers. A final user evaluation comparing ProcessAR and a baseline AR authoring environment showed that, according to our qualitative questionnaire, users preferred ProcessAR. 

BackgroundLow working memory (WM) capacity is associated with alcohol use disorders (AUDs). The importance of WM to adaptive functioning has led to a recent influx of studies attempting to improve individual WM capacity using various cognitive training methods. The present study aimed to examine the efficacy of complex WM training for improving WM capacity among individuals with AUD. MethodsIndividuals were randomized to complete either adaptive WM training or active control training. We applied a methodologically rigorous and structured approach, including a battery of near and moderate transfer measures in those with AUDs and a control group. Additionally, we examined cognitive factors (at baseline) and other predictors of adherence, training task improvement, and transfer. ResultsResults suggest improved WM in individuals with AUDs and controls, as evidenced by improved scores on several transfer measures, after adaptive WM training. However, individuals with AUDs showed poorer adherence and less improvement on the training tasks themselves. Neither IQ, WM, sex, nor condition predicted adherence. Level of training task performance, baseline WM, and IQ predicted transfer task improvement. ConclusionsThis is the first study to rigorously examine both the efficacy of WM training in those with AUDs, and predictors of successful training program adherence and transfer in a large sample. Among study completers, results suggest that AUD status does not predict training improvement and transfer. However, AUD status did predict lower program adherence. WM training was more effective in those with higher cognitive ability at baseline. This study provides direct translation to the development of cognitive interventions for treating AUD.