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1. Typing on Midair Virtual Keyboards: Exploring Visual Designs and Interaction Styles

   https://doi.org/10.1007/978-3-030-85610-6_9  

   Adhikary, Jiban.; Vertanen, Keith (August 2021, IFIP Conference on Human-Computer Interaction INTERACT 2021)

   null (Ed.)

   We investigate typing on a QWERTY keyboard rendered in virtual reality. Our system tracks users’ hands in the virtual environment via a Leap Motion mounted on the front of a head mounted display. This allows typing on an auto-correcting midair keyboard without the need for auxiliary input devices such as gloves or handheld controllers. It supports input via the index fingers of one or both hands. We compare two keyboard designs: a normal QWERTY layout and a split layout. We found users typed at around 16 words-per-minute using one or both index fingers on the normal layout, and about 15 words-per-minute using both index fingers on the split layout. Users had a corrected error rate below 2% in all cases. To explore midair typing with limited or no visual feedback, we had users type on an invisible keyboard. Users typed on this keyboard at 11 words-per-minute at an error rate of 3.3% despite the keyboard providing almost no visual feedback. 

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2. Using Interactive Exercise in Mobile Devices to Support Evidence-based Teaching and Learning

   https://doi.org/10.1145/2899415.2899467  

   Fuad, Mohammad Muztaba; Deb, Debzani; Etim, James; Gloster, Clay (January 2016, Proceedings of the 2016 ACM Conference on Innovation and Technology in Computer Science Education)

   To improve student's class experience, the use of mobile devices has been steadily increasing. However, such use of mobile learning environments in the class is mostly static in nature through content delivery or multiple choice and true/false quiz taking. In CS courses, we need learning environments where students can interact with the problem in a hands-on-approach and instructor can assess their learning skills in real-time with problems having different degree of difficulty. To facilitate such interactive problem solving and real-time assessment using mobile devices, a comprehensive backend system is necessary. This paper presents one such system, named Mobile Response System (MRS) software, associated interactive problem-solving activities, and lessons learned by using it in the CS classrooms. MRS provides instructor with the opportunity of evidence-based teaching by allowing students to perform interactive exercises in their mobile devices with different learning outcomes and by getting an instant feedback on their performance and mental models. MRS is easy-to-use, extensible and can render interactive exercises developed by third-party developers. The student performance data shows its effectiveness in increasing student understanding of difficult concepts and the overall perception of using the software was very positive. 

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3. Application of Reflectance Ratios on High-Resolution Satellite Imagery to Remotely Identify Eucalypt Vegetation

   https://doi.org/10.3390/rs12244079  

   Baranowski, Kelsee; Taylor, Teairah; Lambert, Brian; Bharti, Nita (December 2020, Remote Sensing)

   null (Ed.)

   The scale and accessibility of passive global surveillance have rapidly increased over time. This provides an opportunity to calibrate the performance of models, algorithms, and reflectance ratios between remote-sensing devices. Here, we test the sensitivity and specificity of the Eucalypt chlorophyll-a reflectance ratio (ECARR) and Eucalypt chlorophyll-b reflectance ratio (ECBRR) to remotely identify eucalypt vegetation in Queensland, Australia. We compare the reflectance ratio values from Sentinel-2 and Planet imagery across four sites of known vegetation composition. All imagery was transformed to reflectance values, and Planet imagery was additionally scaled to harmonize across Planet scenes. ECARR can identify eucalypt vegetation remotely with high sensitivity but shows low specificity and is impacted by the density of the vegetation. ECBRR reflectance ratios show similar sensitivity and specificity when identifying eucalypt vegetation but with values an order of magnitude smaller than ECARR. We find that ECARR was better at identifying eucalypt vegetation in the Sentinel-2 imagery than Planet imagery. ECARR can serve as a general chlorophyll indicator but is not a specific index to identify Eucalyptus vegetation with certainty. 

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4. Haptic paradigms for multimodal interactive simulations

   Tennison, J. L.; Greenberg, J.; Moore, E. B.; & Gorlewicz, J. L. (January 2021, Journal on technology and persons with disabilities)

   Touch is often omitted or viewed as unnecessary in digital learning. Lack of touch feedback limits the accessibility and multimodal capacity of digital educational content. Touchscreens with vibratory, haptic feedback are prevalent, yet this kind of feedback is often under-utilized. This work provides initial investigations into the design, development, and use of vibratory feedback within multimodal, interactive, educational simulations on touchscreen devices by learners with and without visual impairments. The objective of this work is to design and evaluate different haptic paradigms that could support interaction and learning in educational simulations. We investigated the implementation of four haptic paradigms in two physics simulations. Interviews were conducted with eight learners (five sighted learners; three learners with visual impairments) on one simulation and initial results are shared. We discuss the learner outcomes of each paradigm and how they impact design and development moving forward. 

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5. Designing and Evaluating Interactive Tools for a Robot Hand Collection

   https://doi.org/10.1145/3568294.3580099  

   Abou_Chahine, Ramzi; Padmanabhuni, Sid; Fazli, Pooyan; Seifi, Hasti (March 2023, Association for Computing Machinery)

   Recent robot collections provide various interactive tools for users to explore and analyze their datasets. Yet, the literature lacks data on how users interact with these collections and which tools can best support their goals. This late-breaking report presents preliminary data on the utility of four interactive tools for accessing a collection of robot hands. The tools include a gallery and similarity comparison for browsing and filtering existing hands, a prediction tool for estimating user impression of hands (e.g., humanlikeness), and a recommendation tool suggesting design features (e.g., number of fingers) for achieving a target user impression rating. Data from a user study with 9 novice robotics researchers suggest the users found the tools useful for various tasks and especially appreciated the gallery and recommendation functionalities for understanding the complex relationships of the data. We discuss the results and outline future steps for developing interface design guidelines for robot collections. 

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