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1. 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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2. 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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3. Building a reinforcement learning environment from limited data to optimize teachable robot interventions.

   Maidment, T.; Yu, M.; Lobczowski, N. G; Kovashka, A.; Walker, E.; Litman, D.; & Nokes-Malach, T. (January 2022, Proceedings of the 15th International Conference on Educational Data Mining)

   Mitrovic, A.; & Bosch, N. (Ed.)

   Working collaboratively in groups can positively impact performance and student engagement. Intelligent social agents can provide a source of personalized support for students, and their benefits likely extend to collaborative settings, but it is difficult to determine how these agents should interact with students. Reinforcement learning (RL) offers an opportunity for adapting the interactions between the social agent and the students to better support collaboration and learning. However, using RL in education with social agents typically involves training using real students. In this work, we train an RL agent in a high-quality simulated environment to learn how to improve students’ collaboration. Data was collected during a pilot study with dyads of students who worked together to tutor an intelligent teachable robot. We explore the process of building an environment from the data, training a policy, and the impact of the policy on different students, compared to various baselines. 

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4. Using Augmented Reality Technology to Support Young Students’ Embodied Learning Experience in Computational Tasks

   Zhou, C.; Kwon, K.; Brush, T.; Kim, K.; Muralidharan, A.; Kim, Y. (January 2022, SITE Interactive Online 2022 Conference)

   Langran, L.; Henriksen, D. (Ed.)

   This study introduces an Augmented-Reality-based learning system that aims to support young students’ embodied learning in block-based programming activities where they learn computational concepts and create meaningful chunks of codes. Students are going to perform episode-embedded path-finding tasks, which are designed to practice their capacities of applying computational thinking in a reasonable manner to solve problems within different scenarios. Grounded on an embodied cognition approach, the AR integration creates a concrete and tangible environment for young students to understand abstract conceptual knowledge in an engaging and interactive way, with a close connection built between the real and virtual worlds. 

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5. Exploring Virtual Agents for Augmented Reality

   https://doi.org/10.1145/3290605.3300511  

   Wang, Isaac; Smith, Jesse; Ruiz, Jaime (May 2019, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems)

   Prior work has shown that embodiment can benefit virtual agents, such as increasing rapport and conveying non-verbal information. However, it is unclear if users prefer an embodied to a speech-only agent for augmented reality (AR) headsets that are designed to assist users in completing real-world tasks. We conducted a study to examine users' perceptions and behaviors when interacting with virtual agents in AR. We asked 24 adults to wear the Microsoft HoloLens and find objects in a hidden object game while interacting with an agent that would offer assistance. We presented participants with four different agents: voice-only, non-human, full-size embodied, and a miniature embodied agent. Overall, users preferred the miniature embodied agent due to the novelty of his size and reduced uncanniness as opposed to the larger agent. From our results, we draw conclusions about how agent representation matters and derive guidelines on designing agents for AR headsets. 

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