Developing a Group-Based Literacy Screening for German Pre-Readers: A Digital, Game-Based ApproachEarly prediction of children's literacy skills is crucial for successful literacy development. However, standardized screenings for pre-readers are mainly paper-based and designed for one-on-one sessions, demanding significant resources. We present the development and feasibility evaluation of a digital, game-based literacy screening for German pre-readers that supports group sessions. The screening comprises five tasks that do not rely on written language skills. We detail critical design decisions and guidelines for the effective implementation of this group-based screening. We evaluated the feasibility and user experience with 34 German second- and third-graders. Results revealed that the screening is suitable for use in group settings and that it was positively perceived by the children. Children found the tasks enganging and straightforward, often perceiving them as games. This study demonstrates that digital game-based screenings can be used effectively in group settings with young children with minimal adult guidance, offering a motivating and engaging assessment method.2024HHHeiko Holz et al.Gamification DesignK-12 Digital Education ToolsSpecial Education TechnologyMobileHCI
Multiperspective Teaching of Unknown Objects via Shared-gaze-based Multimodal Human-Robot InteractionFor successful deployment of robots in multifaceted situations, an understanding of the robot for its environment is indispensable. With advancing performance of state-of-the-art object detectors, the capability of robots to detect objects within their interaction domain is also enhancing. However, it binds the robot to a few trained classes and prevents it from adapting to unfamiliar surroundings beyond predefined scenarios. In such scenarios, humans could assist robots amidst the overwhelming number of interaction entities and impart the requisite expertise by acting as teachers. We propose a novel pipeline that effectively harnesses human gaze and augmented reality in a human-robot collaboration context to teach a robot novel objects in its surrounding environment. By intertwining gaze (to guide the robot's attention to an object of interest) with augmented reality (to convey the respective class information) we enable the robot to quickly acquire a significant amount of automatically labeled training data on its own. Training in a transfer learning fashion, we demonstrate the robot's capability to detect recently learned objects and evaluate the influence of different machine learning models and learning procedures as well as the amount of training data involved. Our multimodal approach proves to be an efficient and natural way to teach the robot novel objects based on a few instances and allows it to detect classes for which no training dataset is available. In addition, we make our dataset publicly available to the research community, which consists of RGB and depth data, intrinsic and extrinsic camera parameters, along with regions of interest.2023DWDaniel Weber et al.Eye Tracking & Gaze InteractionHuman Pose & Activity RecognitionAR Navigation & Context AwarenessHRI
Guilty Artificial Minds: Folk Attributions of Mens Rea and Culpability to Artificially Intelligent Agents While philosophers hold that it is patently absurd to blame robots or hold them morally responsible (e.g. Sparrow, 2007), a series of recent empirical studies suggest that people do ascribe blame to AI systems and robots in certain contexts (e.g. Malle et al. 2016). This is disconcerting: Blame might be shifted from owners, users or designers of AI systems to the systems themselves, leading to the diminished accountability of the responsible human agents (Kneer & Stuart, 2021). In this paper, we explore one of the potential underlying reasons for robot blame, namely the folk’s willingness to ascribe inculpating mental states or “mens rea” to robots. In a vignette-based experiment (N=513), we presented participants with a situation in which an agent knowingly runs the risk of bringing about substantial harm. We manipulated agent type (human v. group agent v. AI-driven robot) and outcome (neutral v. bad), and measured both moral judgment (wrongness of the action and blameworthiness of the agent) and mental states attributed to the agent (recklessness and the desire to inflict harm). We found that (i) judgments of wrongness and blame were relatively similar across agent types, possibly because (ii) judgments of attributed mental states were, as suspected, similar across agent types. This raised the question – also explored in the experiment – whether people attribute knowledge and desire to robots in a metaphorical way (e.g. the robot “knew” rather than actually knew). However, (iii), according to our data people were unwilling to downgrade to mens rea in a merely metaphorical sense. Finally, (iv), we report a surprising and novel finding, which we call the inverse outcome effect on robot blame: People were less willing to blame artificial agents for bad outcomes than for neutral outcomes. This suggests that they are implicitly aware of the dangers of overattributing blame to robots when harm comes to pass, which might lead to inappropriately letting the responsible human agent off the moral hook.2021MSMichael T. Stuart et al.Interpreting and Explaining AICSCW
Development and Evaluation of a Data Privacy Concept for a Frustration-Aware In-Vehicle SystemTo realize frustration-aware in-vehicle systems based on real-time user monitoring, personal data have to be recorded, analyzed and (potentially) stored raising data privacy concerns that may reduce the user acceptance and hence the spread of such systems. Complementing the development of a frustration-aware system with voice interface in the project F-RELACS, a data privacy concept was created based on the principles privacy by design and privacy by default recommended in the European General Data Protection Regulation. Nine criteria were formulated and 23 concrete measures to satisfy the criteria were derived. The measures were evaluated in an online study with 96 participants between 18 and 74 years. On average, the measures were rated as rather sufficient to sufficient. Participants evaluated the use of commercial third-party software for speech processing as most critical. All results are discussed and proposals to further increase the acceptance of frustration-aware systems are outlined.2021KIKlas Ihme et al.Head-Up Display (HUD) & Advanced Driver Assistance Systems (ADAS)Privacy by Design & User ControlAutoUI
Geopositioned 3D Areas of Interest for Gaze AnalysisTo understand driver’s gaze behavior, the gaze is usually matched to surrounding objects or static areas of interest (AOI) at fixed positions around the car. Full surround object tracking allows for an understanding of the traffic situation. However, because it requires an extensive sensor set and a lot of processing power, it’s not yet broadly available in production cars. The use of static AOIs only requires the addition of eye tracking sensors. They are at fixed positions around the car and can't adapt to the environment, therefore their usefulness is limited. We propose geopositioned 3D AOIs. With adaptability and the use of a small sensor set, they combine the strength of both methods. To test 3D AOIs' capabilities for gaze analysis, a driving simulator study with 74 participants was conducted. We show that 3D AOIs are suitable for driver's gaze analysis and a promising tool for driver intention prediction.2021JBJan Bickerdt et al.Eye Tracking & Gaze InteractionAutoUI
Digital Transformations of Classrooms in Virtual RealityWith rapid developments in consumer-level head-mounted displays and computer graphics, immersive VR has the potential to take online and remote learning closer to real-world settings. However, the effects of such digital transformations on learners, particularly for VR, have not been evaluated in depth. This work investigates the interaction-related effects of sitting positions of learners, visualization styles of peer-learners and teachers, and hand-raising behaviors of virtual peer-learners on learners in an immersive VR classroom, using eye tracking data. Our results indicate that learners sitting in the back of the virtual classroom may have difficulties extracting information. Additionally, we find indications that learners engage with lectures more efficiently if virtual avatars are visualized with realistic styles. Lastly, we find different eye movement behaviors towards different performance levels of virtual peer-learners, which should be investigated further. Our findings present an important baseline for design decisions for VR classrooms.2021HGHong Gao et al.University of TübingenSocial & Collaborative VRCollaborative Learning & Peer TeachingCHI
The Low/High Index of Pupillary ActivityA novel eye-tracked measure of pupil diameter oscillation is derived as an indicator of cognitive load. The new metric, termed the Low/High Index of Pupillary Activity (LHIPA), is able to discriminate cognitive load (vis-a-vis task difficulty) in several experiments where the Index of Pupillary Activity fails to do so. Rationale for the LHIPA is tied to the functioning of the human autonomic nervous system yielding a hybrid measure based on the ratio of Low/High frequencies of pupil oscillation. The paper's contribution is twofold. First, full documentation is provided for the calculation of the LHIPA. As with the IPA, it is possible for researchers to apply this metric to their own experiments where a measure of cognitive load is of interest. Second, robustness of the LHIPA is shown in analysis of three experiments, a restrictive fixed-gaze number counting task, a less restrictive fixed-gaze n-back task, and an applied eye-typing task.2020ADAndrew T. Duchowski et al.Clemson UniversityEye Tracking & Gaze InteractionVisualization Perception & CognitionCHI
P6 - Looming Auditory Collision Warnings for Semi-Automated Driving: An EEG/ERP StudyLooming sounds can be an ideal warning notification for emergency braking. This agrees with studies that have consistently demonstrated preferential brain processing for looming stimuli. This study investigates and demonstrates that looming sounds can similarly benefit emergency braking in managing a vehicle with adaptive cruise control (ACC). Specifically, looming auditory notifications induced the faster emergency braking times relative to a static auditory notification. Next, we compare the event-related potential (ERP) evoked by a looming notification, relative to its static equivalent. Looming notifications evoke a smaller fronto-central N2 amplitude than their static equivalents. Thus, we infer that looming sounds are consistent with the visual experience of an approaching collision and, hence, induced a corresponding performance benefit. Subjective ratings indicate no significant differences in the perceived workload across the notification conditions. Overall, this work suggests that auditory warnings should have congruent physical properties with the visual events that they warn for.2018MLMarie Lahmer et al.Head-Up Display (HUD) & Advanced Driver Assistance Systems (ADAS)Voice User Interface (VUI) DesignAutoUI
Feel the Movement: Real Motion Influences Responses to Take-over Requests in Highly Automated VehiclesTake-over requests (TORs) in highly automated vehicles are cues that prompt users to resume control. TORs however, are often evaluated in non-moving driving simulators. This ignores the role of motion, an important source of information for users who have their eyes off the road while engaged in non-driving related tasks. We ran a user study in a moving-base driving simulator to investigate the effect of motion on TOR responses. We found that with motion, user responses to TORs vary depending on the road context where TORs are issued. While previous work showed that participants are fast to respond to urgent cues, we show that this is true only when TORs are presented on straight roads. Urgent cues issued on curved roads elicit slower responses than non-urgent cues on curved roads. Our findings indicate that TORs should be designed to be aware of road context to accommodate natural user responses.2018SBShadan Sadeghian Borojeni et al.OFFIS Institute for Information TechnologyAutomated Driving Interface & Takeover DesignCHI