Learning with simulated virtual classmates: Effects of social-related configurations on students' visual attention and learning experiences in an immersive virtual reality classroom

This systematic review study synthesizes research findings pertaining to the use of eye-tracking technology in immersive virtual reality (IVR) learning environments created by using head mounted displays. Research published between January 2012 and May 2022 has been explored and specific inclusion and exclusion criteria have been applied to select articles. Fifty articles were selected and then reviewed with respect to (1) devices employed in research; (2) learning domains; (3) the number and academic level of participants, data collection methods, and duration of IVR activities; and (4) indicators and themes of visual attention. The main findings from this review demonstrate Tobii and HTC Vive are the most popular tools in research on eye-tracking technology usage in IVR learning environments. What's more, cognitive science and educational technology were found to be the most frequent domains in research on eye-tracking technology usage in IVR. The number of participants varied across reviewed articles but tertiary education was the most common academic level of participating students. Scholars used questionnaires and tests frequently to collect their research data. Fixation duration was the most frequently used indicator. The most frequent themes in reviewed research were task performance, teaching and learning strategies, and learning tools. The study concludes with suggestions for future research. • We reviewed thirty-six articles on eye-tracking technology in IVR. • Our focus was on devices, domains, methods, indicators and themes in research. • Future research directions were also provided. 

A review study on eye-tracking technology usage in immersive virtual reality learning environments

Abstract Understanding existing gender differences in the development of computational thinking skills is increasingly important for gaining valuable insights into bridging the gender gap. However, there are few studies to date that have examined gender differences based on the learning process in a realistic classroom context. In this work, we aim to investigate gender classification using students’ eye movements that reflect temporal human behavior during a computational thinking lesson in an immersive VR classroom. We trained several machine learning classifiers and showed that students’ eye movements provide discriminative information for gender classification. In addition, we employed a Shapley additive explanation (SHAP) approach for feature selection and further model interpretation. The classification model trained with the selected (best) eye movement feature set using SHAP achieved improved performance with an average accuracy of over $$70\%$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mn>70</mml:mn> <mml:mo>%</mml:mo> </mml:mrow> </mml:math> . The SHAP values further explained the classification model by identifying important features and their impacts on the model output, namely gender. Our findings provide insights into the use of eye movements for in-depth investigations of gender differences in learning activities in VR classroom setups that are ecologically valid and may provide clues for providing personalized learning support and tutoring in such educational systems or optimizing system design. 

/pdf/exploring-gender-differences-in-computational-thinking-1lmfbhqn.pdf

Exploring Gender Differences in Computational Thinking Learning in a VR Classroom: Developing Machine Learning Models Using Eye-Tracking Data and Explaining the Models

Robot teleoperation, a control method allowing human operators to manipulate robotic systems remotely, has become increasingly popular in construction applications. A significant challenge is the disconnection between the robot sensor data and the human operator’s sensory processes, creating a sensorimotor mismatch in motor-intensive activities. This disconnection is particularly challenging in motor-intensive activities that require accurate perception and response. Researchers have started investigating haptic interactions to enhance the control feedback loop, including simulating contacts, motions, and tactile input. However, although current methodologies have advanced the field, they often focused on certain aspects and could be further expanded to provide a more comprehensive simulation of the physical interaction that occurs in typical construction operations. This study designs and tests a comprehensive high-fidelity embodied teleoperation method that simulates complete real-world physical processes via the physics engine. The proposed method captures all categories of physical interaction in typical motor-intensive construction tasks, including weight, texture, inertia, impact, balance, rotation, and spring. A human-subject experiment shows that the proposed method substantially improves performance and human functions in a teleoperated pipe-fitting task. The results indicate that the proposed multisensory augmentation method significantly enhances performance and user experience, offering valuable insights for designing innovative robot teleoperation systems for future construction applications. 

Embodied Robot Teleoperation Based on High-Fidelity Visual-Haptic Simulator: Pipe-Fitting Example

Recent developments in computer graphics, hardware, artificial intelligence (AI), and human-computer interaction likely lead to extended reality (XR) devices and setups being more pervasive. While these devices and setups provide users with interactive, engaging, and immersive experiences with different sensing modalities, such as eye and hand trackers, many non-player characters are utilized in a pre-scripted way or by conventional AI techniques. In this paper, we argue for using large language models (LLMs) in XR by embedding them in virtual avatars or as narratives to facilitate more inclusive experiences through prompt engineering according to user profiles and fine-tuning the LLMs for particular purposes. We argue that such inclusion will facilitate diversity for XR use. In addition, we believe that with the versatile conversational capabilities of LLMs, users will engage more with XR environments, which might help XR be more used in everyday life. Lastly, we speculate that combining the information provided to LLM-powered environments by the users and the biometric data obtained through the sensors might lead to novel privacy invasions. While studying such possible privacy invasions, user privacy concerns and preferences should also be investigated. In summary, despite some challenges, embedding LLMs into XR is a promising and novel research area with several opportunities.

pdf/embedding-large-language-models-into-extended-reality-3qmakyaelc.pdf

Embedding Large Language Models into Extended Reality: Opportunities and Challenges for Inclusion, Engagement, and Privacy

Recent developments in computer graphics and hardware technology enable easy access to virtual reality headsets along with integrated eye trackers, leading to mass usage of such devices. The immersive experience provided by virtual reality and the possibility to control environmental factors in virtual setups may soon help to create realistic digital alternatives to conventional classrooms. The importance of such settings has become especially evident during the COVID-19 pandemic, forcing many schools and universities to provide the digital teaching. Researchers foresee that such transformations will continue in the future with virtual worlds becoming an integral part of education. Until now, however, students' behaviors in immersive virtual environments have not been investigated in depth. In this work, we study students' attention by exploiting object-of-interests using eye tracking in different classroom manipulations. More specifically, we varied sitting positions of students, visualization styles of virtual avatars, and hand-raising percentages of peer-learners. Our empirical evidence shows that such manipulations play an important role in students' attention towards virtual peer-learners, instructors, and lecture material. This research may contribute to understanding of how visual attention relates to social dynamics in the virtual classroom, including significant considerations for the design of virtual learning spaces.

Exploiting Object-of-Interest Information to Understand Attention in VR Classrooms

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Exploiting Object-of-Interest Information to Understand Attention in VR Classrooms