This paper presents insights from a Research through Design investigation exploring the integration of tangible and embodied interaction (TEI) technologies into Montessori elementary mathematics education. Unlike traditional classrooms, Montessori prioritizes self-directed exploration, mixed-age groups, and hands-on engagement with physical manipulatives. While prior research shows TEI can support math learning in traditional classrooms, its use in Montessori settings is underexplored. This paper reports on the foundational phase of a Research through Design (RtD) study with ten Montessori teachers. Our process began with foundational inquiry, including semi-structured interviews, classroom observations, and visioning workshops with teachers, which guided the development of a proof-of-concept prototype. This prototype served as a central artifact for inquiry, enabling collaborative exploration and refinement during iterative sessions with each teacher. Through this design-led inquiry, our qualitative analysis identifies initial considerations for supporting individualized learning, collaboration, and physical experiences.

Social Virtual Reality (VR) is emerging as a promising technology for individuals with Attention-Deficit/Hyperactivity Disorder (ADHD).However, we still need to explore how practitioners perceive and envision its integration into their clinical practice.This study presents findings from semi-structured interviews with six practitioners (clinicians, therapists, and clinical professors) experienced in ADHD and VR interventions.Our findings reveal practitioners see significant therapeutic potential in social VR for supporting cognitive skills, impulse control, and creating emotionally safe environments.However, they also highlight challenges, including difficulties with social communication in VR, ensuring skill transfer to real-life, and managing overstimulation.

Findings from embodied cognition suggest that our whole body (not just our eyes) plays an important role in how we make sense of data when we interact with data visualizations. In this paper, we present the results of a study that explores how different designs of the ”interaction” (with a data visualization) alter the way in which people report and discuss correlation and causation in data. We conducted a lab study with two experimental conditions: Full body (participants interacted with a 65” display showing geo-referenced data using gestures and body movements); and, Gamepad (people used a joypad to control the system). Participants tended to agree less with statements that portray correlation and causation in data after using the Gamepad system. Additionally, discourse analysis based on Conceptual Metaphor Theory revealed that users made fewer remarks based on FORCE schemata in Gamepad than in Full-Body.

Learning ballet is a complex motor task that can be effectively enhanced by technology. Learning technologies, however, are not typically used for the assessment of ballet technique due to a lack of adequate and non-invasive tools that can be pragmatically adopted. We conducted an interview-based qualitative study with seven expert ballet teachers and six pre-professional dancers to examine their current and future technology use in a ballet technique class. Through inductive and deductive analysis, we identified reasons for technology non-use and derived seven requirements that can inform the design and implementation of ballet assessment technologies including designing for: adaptation to multi-skill/multi-method environments, teacher/dancer skill augmentation, agency, non-invasive design, feedback for gross/fine movements, trust, and proprioception by supporting transformative assessment. We discuss barriers for technology acceptance and unintended consequences that should be considered when designing future technologies for ballet.

Even when they are able to secure employment, people with cognitive disabilities typically encounter significant difficulties in the workplace. In this paper, we focus on Mixed-Ability workplaces: work settings in which people without disabilities and with different types of disabilities collaborate on a daily basis. The case study for our exploratory research is a university library that has been able to support a mixed-ability work setting for over four years. We describe how a theory from cognitive linguistics (Conceptual Metaphor Theory) can be used to explore the challenges that people encounter in mixed-ability workplaces, identify the cognitive processes that differ between neurotypical team leaders and workers with cognitive disabilities, and translate these findings into design recommendations for embodied technologies that support mixed-ability workplaces.

Museums have embraced embodied interaction: its novelty generates buzz and excitement among their patrons, and it has enormous educational potential. Human-Data Interaction (HDI) is a class of embodied interactions that enables people to explore large sets of data using interactive visualizations that users control with gestures and body movements. In museums, however, HDI installations have no utility if visitors do not engage with them. In this paper, we present a quasi-experimental study that investigates how different ways of representing the user ("mode type") next-to a data visualization alters the way in which people engage with a HDI system. We consider four mode types: avatar, skeleton, camera overlay, and control. Our findings indicate that the mode type impacts the number of visitors that interact with the installation, the gestures that people do, and the amount of time that visitors spend observing the data on display and interacting with the system.

Embodied interaction is particularly useful in museums because it allows to leverage findings from embodied cognition to support the learning of STEM concepts and thinking skills. In this paper, we focus on Human-Data Interaction (HDI), a class of embodied interactions that investigates the design of interactive data visualizations that users control with gestures and body movements. We describe an HDI system that we iteratively designed, implemented, and observed at a science museum, and that allows visitors to explore large sets of data on two 3D globe maps. We present and discuss design strategies and optimization that we implemented to mitigate two sets of design challenges: (1) Dealing with display, interaction, and affordance blindness; and, (2) Supporting multiple functionalities and collaboration.

Dancer-teacher communication in a ballet class can be challenging: ballet is one of the most complex forms of movements, and learning happens through multi-faceted interactions with studio tools (mirror, barre, and floor) and the teacher. We conducted an interview-based qualitative study with seven ballet teachers and six dancers followed by an open-coded analysis to explore the communication challenges that arise while teaching and learning in the ballet studio. We identified key communication issues, including adapting to multi-level dancer expertise, transmitting and realigning development goals, providing personalized corrections and feedback, maintaining the state of flow, and communicating how to properly use tools in the environment. We discuss design implications for crafting technological interventions aimed at mitigating these communication challenges.

The wide availability of body-sensing technologies (such as Nintendo Wii and Microsoft Kinect) has the potential to bring full-body interaction to the masses, but the design of hand gestures and body movements that can be easily discovered by the users of such systems is still a challenge. In this paper, we revise and evaluate Framed Guessability, a design methodology for crafting discoverable hand gestures and body movements that focuses participants' suggestions within a "frame," i.e. a scenario. We elicited gestures and body movements via the Guessability and the Framed Guessability methods, consulting 89 participants in-lab. We then conducted an in-situ quasi-experimental study with 138 museum visitors to compare the discoverability of gestures and body movements elicited with these two methods. We found that the Framed Guessability movements were more discoverable than those generated via traditional Guessability, even though in the museum there was no reference to the frame.

Engaging museum visitors in data exploration using full-body interaction is still a challenge. In this paper, we explore four strategies for providing entry-points to the interaction: instrumenting the floor; forcing collaboration; implementing multiple body movements to control the same effect; and, visualizing the visitors' silhouette beside the data visualization. We discuss preliminary results of an in-situ study with 56 museum visitors at Discovery Place, and provide design recommendations for crafting engaging Human-Data Interaction experiences.

The "Uncanny Valley" theory explains the counter-intuitive phenomenon where people may get suddenly uncomfortable with an artificial entity when it becomes very similar to humans. We propose the existence of an "uncanny valley" for embodied interaction, when a user's body motions in the physical space (the locus of interaction) are incompletely mapped into effects in the virtual space (the focus of interaction). It is generally assumed that this mapping should be as veridical as possible to promote seamless embodied interaction. Many design factors (e.g., synchronicity, sensitivity, shared realism) contribute to veridical locus-focus mapping. We intentionally varied the level of veridicality of these different factors, affecting how the user's movements were mapped to virtual effects. Our results indicate that there is a dip (valley) in user preferences when the design contains mixed degrees of veridicality. Thus, when one veridical dimension is limited, designers should likewise reduce the veridicality of other dimensions.

As data rather than physical artifacts become more commonly the product of modern scientific endeavor, we must attend to humandata interactions as people reason about and with representations of data increasingly being presented in museum settings. Complex data sets can be impenetrable for novices, so the exhibit presented here was designed to give visitors control over a personalized "slice" of the data set as an entry point for exploration. Personalized control and collaboration can often be at odds in exhibits, however. This paper presents a study of two alternate approaches to designing an embodied interaction control for the exhibit that serves both needs. The results demonstrate that interaction design can affect children's perspective taking as they interact with a Census data map museum display, and that the perspective taken by individuals is correlated with their operation of the interactive exhibit and the kinds of reasoning they employ while investigating data.

Despite the wide availability of body-sensing technologies, the design of control gestures that feel natural and that can be intuitively "guessed" by the users is still an embodied interaction challenge. This is especially true for systems that require a set of complementary control gestures. Part of the problem lies in the separation between the locus of the interaction (the body) and the focus of the interaction (the screen). We extend Johnson's theory of Embodied Schemata with Embodied Allegories, in order to create a unifying context that spans across the locus and focus of interaction. We present results that demonstrate how this approach increases the chance that users select the same gesture or movement for producing an effect within the virtual context, and that the resultant gesture set is deemed more intuitive by users. We also present the accompanying methodology, "Framed Guessability," which can increase users' agreement when conducting Guessability Studies.

Museums are increasingly embracing technologies that provide highly-individualized and highly-interactive experiences to visitors. With embodied interaction experiences, increased localization accuracy supports greater nuance in interaction design, but there is usually a tradeoff between fast, accurate tracking and the ability to preserve the identity of users. Customization of experience relies on the ability to detect the identity of visitors, however. We present a method that combines fine-grained indoor tracking with robust preservation of the unique identities of multiple users. Our model merges input from an RFID reader with input from a commercial camera-based tracking system. We developed a probabilistic Bayesian model to infer at run-time the correct identification of the subjects in the camera's field of view. This method, tested in a lab and at a local museum, requires minimal modification to the exhibition space, while addressing several identity-preservation problems for which many indoor tracking systems do not have robust solutions.

Human-Data Interaction (HDI) systems can be defined as technologies that use embodied interaction to facilitate the users' exploration of rich datasets. As the design of gestures for Whole-Body Interaction is often based on an uninformed trial and error approach, I propose the use of Johnson's embodied schemata, extended with allegories, to inform the design of suites of gestures for HDI systems. This approach involves: (1) the identification of embodied allegories to encourage data exploration, (2) a study of the allegorical relation between input and output, and (3) an analysis of the implication of the (social) space.