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1. Embodied Learning in a Mixed-Reality Environment: Examination of Student Embodiment

   Kwon, Kyungbin; Kim, Keunjae; Seo, Minhwi; Kim, Hyojung; Brush, Thomas (March 2024, Association for the Advancement of Computing in Education (AACE))

   Cohen, J; Solano, G (Ed.)

   This study investigates the effects of embodied learning experiences in learning abstract concepts, such as computational thinking (CT), among young learners. Specifically, it examines whether the benefits of embodied learning can be replicated within a mixed-reality setting, where students engage with virtual objects to perform CT tasks. A group of ten first-grade students from an elementary school participated, engaging in embodied learning activities followed by assessments in CT. Through the analysis of video recordings, it was observed that participants could effectively articulate CT concepts, including the understanding of programming code meanings and their sequences, through their bodily movements. The congruence between students’ bodily movement and CT concepts was found to be advantageous for their comprehension. However, the study also noted instances of incongruent movements that did not align with the intended CT concepts, which attracted researchers’ attentions. The study identified two distinct types of embodiment manifested in the mixed-reality environment, shedding light on the nuanced dynamics of embodied learning in the context of CT education. 

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2. Expanding Opportunities for Systems Thinking, Conceptual Learning, and Participation through Embodied and Computational Modeling

   https://doi.org/10.3390/systems8040048  

   Pierson, Ashlyn E.; Brady, Corey E. (December 2020, Systems)

   Previous research has established that embodied modeling (role-playing agents in a system) can support learning about complexity. Separately, research has demonstrated that increasing the multimodal resources available to students can support sensemaking, particularly for students classified as English Learners. This study bridges these two bodies of research to consider how embodied models can strengthen an interconnected system of multimodal models created by a classroom. We explore how iteratively refining embodied modeling activities strengthened connections to other models, real-world phenomena, and multimodal representations. Through design-based research in a sixth grade classroom studying ecosystems, we refined embodied modeling activities initially conceived as supports for computational thinking and modeling. Across three iterative cycles, we illustrate how the conceptual and epistemic relationship between the computational and embodied model shifted, and we analyze how these shifts shaped opportunities for learning and participation by: (1) recognizing each student’s perspectives as critical for making sense of the model, (2) encouraging students to question and modify the “code” for the model, and (3) leveraging multimodal resources, including graphs, gestures, and student-generated language, for meaning-making. Through these shifts, the embodied model became a full-fledged component of the classroom’s model system and created more equitable opportunities for learning and participation. 

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3. Learning computational thinking through embodied experience: a proposal of a framework

   Kim, Hyojung; Kwon, Kyungbin (March 2024, Association for the Advancement of Computing in Education (AACE))

   Cohen, J; Solano, G (Ed.)

   This study is implemented with a focus of discovering how students use the practice of embodied learning to gain knowledge of computational thinking (CT). An intervention was executed at an elementary school in a midwestern state, where students used a marker free virtual reality system to engage in a task that requires them to use the CT concepts and skills. Students participated in the path finding activity within the AR system, and demonstrated accounts of how they use their body to express their understanding of abstract CT concepts. Moreover, the affordances of the AR system were integrated to the student’s learning experience, furthering the discussion of how student’s embodied movement within the virtual world influences their learning outcomes of CT concepts. As an attempt to analyze the embodied learning experience of abstract notions, the researchers developed a coding framework that introduces the mapping of abstract CT concepts and the tangible embodied action that reflects each concept. This short paper thus presents the framework for embodied computational thinking skills, and further elaborates on the future implications of the on-going work. 

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4. Embodied Learning for Computational Thinking in a Mixed-Reality Context

   https://doi.org/10.1177/07356331241291173  

   Kwon, Kyungbin; Brush, Thomas_A; Kim, Keunjae; Seo, Minhwi (October 2024, Journal of Educational Computing Research)

   This study examined the effects of embodied learning experiences on students’ understanding of computational thinking (CT) concepts and their ability to solve CT problems. In a mixed-reality learning environment, students mapped CT concepts, such as sequencing and loops, onto their bodily movements. These movements were later applied to robot programming tasks, where students used the same CT concepts in a different modality. By explicitly connecting embodied actions with programming tasks, the intervention aimed to enhance students’ comprehension and transfer of CT skills. Forty-four first- and second-grade students participated in the study. The results showed significant improvements in students’ CT competency and positive attitudes toward CT. Additionally, an analysis of robot programming performance identified common errors and revealed how students employed embodied strategies to overcome challenges. The effects of embodied learning and the impact of embodied learning strategies were discussed. 

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5. Briteller: Shining a Light on AI Recommendations for Children

   https://doi.org/10.1145/3706598.3714106  

   Zhou, Xiaofei; Zhang, Yi; Jiang, Yufei; Gong, Yunfan; Zhang, Chi; Antle, Alissa N; Bai, Zhen (April 2025, ACM)

   Understanding howAI recommendationswork can help the younger generation become more informed and critical consumers of the vast amount of information they encounter daily. However, young learners with limited math and computing knowledge often find AI concepts too abstract. To address this, we developed Briteller, a light-based recommendation system that makes learning tangible. By exploring and manipulating light beams, Briteller enables children to understand an AI recommender system’s core algorithmic building block, the dot product, through hands-on interactions. Initial evaluations with ten middle school students demonstrated the effectiveness of this approach, using embodied metaphors, such as "merging light" to represent addition. To overcome the limitations of the physical optical setup, we further explored how AR could embody multiplication, expand data vectors with more attributes, and enhance contextual understanding. Our findings provide valuable insights for designing embodied and tangible learning experiences that make AI concepts more accessible to young learners. 

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