Smart Cities already surround us, and yet they are still incomprehensibly far from directly impacting everyday life. While current Smart Cities are often inaccessible, the experience of everyday citizens may be enhanced with a combination of the emerging technologies Digital Twins (DTs) and Situated Analytics. DTs represent their Physical Twin (PT) in the real world via models, simulations, (remotely) sensed data, context awareness, and interactions. However, interaction requires appropriate interfaces to address the complexity of the city. Ultimately, leveraging the potential of Smart Cities requires going beyond assembling the DT to be comprehensive and accessible. Situated Analytics allows for the anchoring of city information in its spatial context. We advance the concept of embedding the DT into the PT through Situated Analytics to form Fused Twins (FTs). This fusion allows access to data in the location that it is generated in in an embodied context that can make the data more understandable. Prototypes of FTs are rapidly emerging from different domains, but Smart Cities represent the context with the most potential for FTs in the future. This paper reviews DTs, Situated Analytics, and Smart Cities as the foundations of FTs. Regarding DTs, we define five components (physical, data, analytical, virtual, and Connection Environments) that we relate to several cognates (i.e., similar but different terms) from existing literature. Regarding Situated Analytics, we review the effects of user embodiment on cognition and cognitive load. Finally, we classify existing partial examples of FTs from the literature and address their construction from Augmented Reality, Geographic Information Systems, Building/City Information Models, and DTs and provide an overview of future directions.

With the fast evolution of Virtual Reality (vr) technology, new prospects opened for embodied learning. Learners can now manipulate digital representations of abstract concepts and make sense of them through sensorimotor stimulation. However, in research, embodiment is explored from several perspectives, which, we argue, should be considered within a same framework. In this paper, we describe three major perspectives relevant for embodied learning in Virtual Reality (vr): embodied cognition, embodied interaction, and avatar embodiment. We organize these perspectives within one common interdisciplinary framework, and discuss resulting design opportunities for vr embodied learning interactions. Specifically, we show that embodied interaction does not necessarily support embodied cognition, and that breaking recommendations of avatar embodiment can actually support meaning-making. We believe our work offers novel avenues for future research and will foster interesting conversations in the hci community.

Background: Visualization technology is enhancing interactive learning by merging digital content with real-world environments, offering immersive experiences through augmented reality (AR) in fields like medical education. AR is being increasingly used in medicine and dental education to improve student learning, particularly in understanding complex concepts such as bone remodeling. Active learning strategies, supported by AR, boost student autonomy, reduce cognitive load, and improve learning outcomes across various disciplines. AR is gaining popularity in higher education as it enhances active learning, reduces cognitive load, and improves cognitive, meta-cognitive, and affective outcomes, particularly in medical and nursing education. The effectiveness of immersive AR in enhancing understanding of complex physiological processes is still unclear, with a lack of rigorous studies on its impact and how to effectively convert academic content into AR. Objective: We assess the capacity of AR-enhanced content for learning medical knowledge with a state-of-the-art AR game published along with a modern cell atlas of the oral cavity. To assess AR-enhanced content for learning, we formulated hypotheses on the general impact on learning (H1), specific improvements in learning (H2), and long-term retention (H3). Methods: An AR serious game was developed to represent current knowledge on osteoclasts for classroom use. The game was evaluated in an unblinded face-to-face vignette experiment (39 participants). Learning outcomes on “Osteoclasts” were compared between the AR game (17 participants) and a textbook-only option (20 participants) conveying the same content. Participants were randomly assigned and learning success was measured at three time-points, immediately after the experiment session, 1 week later, and 1 month later, via web-based surveys. Results: The AR serious game elicited strong interest in the topic (perceived relevance in Attention, Relevance, Confidence, and Satisfaction [ARCS], W=10,417; P<.001) and motivated students by increasing self-efficacy (confidence in ARCS, W=11,882.5; P=.02) and satisfaction (in ARCS, W=4561; P<.001). The learning outcomes were comparable to text-based self-learning (t=2.0103; P_Bonferroni=.095). Furthermore, curious students benefited more from interactive learning methods compared with text-only methods and had higher learning success (t=−2.518; P=.02). Conclusions: Introducing new technology such as AR into teaching requires technological investment, updated curricula, and careful application of learning paradigms. We found support for improved motivation (H1) and some evidence of AR’s baseline effectiveness (H2a). While we could not confirm AR’s impact on visual tasks overall (H2b), we noted an interesting interaction between curiosity and visual task outcomes (H2c), as well as how game design influences student perception of the material (H2d). Due to attrition, long-term learning outcomes (H3) could not be assessed. AR-based learning may particularly benefit curious students, who often struggle with text-heavy methods. As students are increasingly accustomed to brief, engaging content, teaching approaches must adapt.

Mathematics can be difficult to grasp, in part because it relies on symbols and formalisms that are powerful yet meaningless to novices unless grounded in concreteness. Although a wide corpus of research focuses on concreteness in mathematics education, the notion of concreteness can be apprehended in various ways and it is not yet clear which specific aspects of concreteness help the learners. In this paper, we explore embodiment as a form of concreteness to ground abstract mathematics. First, we designed and evaluated an embodied learning activity on graph theory. Through a user study with 89 participants, we then compared three approaches: abstraction, manipulated concreteness, and embodied concreteness. Our results show that, compared to abstraction, both forms of concreteness increase learners’ perceived attention, confidence, and satisfaction. However, only embodied concreteness increases perceived relevance and grounding. Moreover, unlike manipulated concreteness, embodied concreteness does not impair learning outcomes nor transfer abilities.

Concreteness and abstraction are key in educational research in mathematics, both when discussing the nature of mathematics in itself, but also when exploring how to learn and teach mathematics. However, while the terms concrete and abstract are often used in the field, they are not always used with the same meaning. For example, the word concrete can be used to mean specific , relatable , visual , and tangible , while the word abstract can be used as general , rigorous , vague , and symbolic . While several scholars have emphasized the need for a multidimensional and fine-grained framework to articulate these differences in meaning, we further argue that it is crucial to precisely define how the terms concrete and abstract are actually used by the research community. Towards this goal, we offer three contributions. First, we empirically and systematically identify the various meanings of concrete and abstract used in the literature. Second, we offer a data-informed taxonomy to organize this semantic landscape and support research inquiry. And third, we offer templates for the design of future mathematics education interventions and studies.

Effective and timely feedback in educational assessments is essential but labor-intensive, especially for complex tasks. Recent developments in automated feedback systems, ranging from deterministic response grading to the evaluation of semi-open and open-ended essays, have been facilitated by advances in machine learning. The emergence of pre-trained Large Language Models, such as GPT-4, offers promising new opportunities for efficiently processing diverse response types with minimal customization. This study evaluates the effectiveness of a pre-trained GPT-4 model in grading semi-open handwritten responses in a university-level mathematics exam. Our findings indicate that GPT-4 provides surprisingly reliable and cost-effective initial grading, subject to subsequent human verification. Future research should focus on refining grading rules and enhancing the extraction of handwritten responses to further leverage these technologies.