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While 3D printing may be a promising tool for making Science, Technology, Engineering, and Mathematics (STEM) education more accessible for students with visual impairments, most research centers on creating and using tactile models and braille, rather than direct student use of 3D printing technologies. This study observed 121 high school studentswith visual impairments across twelve states, examining whether and how students with visual impairments engage in scientific and engineering practices during their assembling of a 3D printer. We found that students exhibited all eight of the science and engineering behaviors defined in the National Research Council's A Framework for K-12 Science Education:Practices, Crosscutting Concepts, and Core Ideas. This study builds upon the work of Hilson and Wild and shows that students with visual impairments, when given the opportunity, can demonstrate scientific and engineering process skills just as their sighted peers do. This is the largest sample of students with visual impairments to date to be observed to document their work and behaviors in this area of STEM research. However, further research is needed to examine science and engineering behaviors of students with visual impairments in other STEM areas and while completing other complex STEM tasks. 

Although various navigation apps are available, people who are blind or have low vision (PVIB) still face challenges to locate store entrances due to missing geospatial information in existing map services. Previously, we have developed a crowdsourcing platform to collect storefront accessibility and localization data to address the above challenges. In this paper, we have significantly improved the efficiency of data collection and user engagement in our new AI-enabled Smart DoorFront platform by designing and developing multiple important features, including a gamified credit ranking system, a volunteer contribution estimator, an AI-based pre-labeling function, and an image gallery feature. For achieving these, we integrate a specially designed deep learning model called MultiCLU into the Smart DoorFront. We also introduce an online machine learning mechanism to iteratively train the MultiCLU model, by using newly labeled storefront accessibility objects and their locations in images. Our new DoorFront platform not only significantly improves the efficiency of storefront accessibility data collection, but optimizes user experience. We have conducted interviews with six adults who are blind to better understand their daily travel challenges and their feedback indicated that the storefront accessibility data collected via the DoorFront platform would be very beneficial for them.