Mixed-reality learning environments in teacher education: An analysis of TeachLivE™ research

TeachLivE™, a mixed-reality simulated classroom technology, has been used in initial teacher education programs to provide repeatable experiential learning opportunities for students now for more than a decade and in more than 80 universities worldwide. However, no broad scale investigation has been conducted into how the platform has been used or what research has been generated as a result. The aim of this study is to provide insight into the types of TeachLivE™ research carried out since its inception and to identify trends and potential gaps in this research. Peer-reviewed academic primary research publications—journal articles (23), conference proceedings (12), and thesis dissertations (20)—were reviewed for participants, research methods, analysis, research design, data collection tools, and design approaches. Of the 102 articles identified as relevant, “instructional skills development” and “integration of TeachLivE™ in teacher education” were the most commonly researched topics. Findings indicate that preservice teachers were the most commonly studied group of participants, research methods were predominately qualitative, single-subject experimental research design was employed most often, and the most used data collection tools were surveys and observation. These findings highlight that the range of topics is increasing, with studies on in-service teachers in school-based contexts beginning to emerge as a new area of interest. This systematic review has implications for researchers and the developers of TeachLivE™. It provides valuable insight and recommendations for future studies in this emerging teacher education field, where technology is not simply used “in the classroom” but rather “as the classroom.

Bringing Digital Well-Being into the Heart of Digital Media Literacies

The complexities of our digital media landscape present challenges that often strain the physical, emotional, and social well-being of learners and educators alike. Given these challenges, this essay makes a case for incorporating digital well-being into digital and media literacy curricula and pedagogy. For the author, a focus on digital well-being, or the capacity to pursue health, safety, and happiness online, has sparked a shift in pedagogical values and goals. Following a discussion of the nature of digital well-being, the author charts this shift through an example lesson about online identity. Bringing digital well-being into the heart of digital media literacies means reconsidering both the ‘what’ and ‘how’ of our teaching

Preservice teachers’ confidence and preferred teaching strategies using TeachLivE™ Virtual Learning Environment: A two-step cluster analysis

TeachLivETM, a mixed reality learning environment originating from University of Central Florida (2011), has recently been introduced to the Australian preservice teaching context by Murdoch University (2016) and the University of Newcastle (2017). This paper, the first of a program of research mapping the implementation of TeachLivETM within the Australian context, captures preservice teachers’ (PSTs) reflections on their initial interactions with the mixed reality learning environment. The study highlights preferred teaching strategies and teaching confidences during initial interactions in the simulation laboratory and introduces a quality measure within the reflective practice process. A Two-Step Cluster analysis of 322 PSTs was conducted. Results showed a positive impact of reflective practice and revealed that most preservice teachers preferred ‘Questioning’ and ‘Direct Instruction’ methods of delivering micro-teaching lessons. The authors offer a typology of teaching strategies, confidences and a quality measure for teacher educators

Gesture Assessment of Teachers in an Immersive Rehearsal Environment

Interactive training environments typically include feedback mechanisms designed to help trainees improve their performance through either guided- or self-reflection. When the training system deals with human-to-human communications, as one would find in a teacher, counselor, enterprise culture or cross-cultural trainer, such feedback needs to focus on all aspects of human communication. This means that, in addition to verbal communication, nonverbal messages must be captured and analyzed for semantic meaning. The goal of this dissertation is to employ machine-learning algorithms that semi-automate and, where supported, automate event tagging in training systems developed to improve human-to-human interaction. The specific context in which we prototype and validate these models is the TeachLivE teacher rehearsal environment developed at the University of Central Florida. The choice of this environment was governed by its availability, large user population, extensibility and existing reflection tools found within the AMITIES framework underlying the TeachLivE system. Our contribution includes accuracy improvement of the existing data-driven gesture recognition utility from Microsoft; called Visual Gesture Builder. Using this proposed methodology and tracking sensors, we created a gesture database and used it for the implementation of our proposed online gesture recognition and feedback application. We also investigated multiple methods of feedback provision, including visual and haptics. The results from the conducted user studies indicate the positive impact of the proposed feedback applications and informed body language in teaching competency. In this dissertation, we describe the context in which the algorithms have been developed, the importance of recognizing nonverbal communication in this context, the means of providing semi- and fully-automated feedback associated with nonverbal messaging, and a series of preliminary studies developed to inform the research. Furthermore, we outline future research directions on new case studies, and multimodal annotation and analysis, in order to understand the synchrony of acoustic features and gestures in teaching context

Avatar led interventions in the Metaverse reveal that interpersonal effectiveness can be measured, predicted, and improved

Experiential learning has been known to be an engaging and effective modality for personal and professional development. The Metaverse provides ample opportunities for the creation of environments in which such experiential learning can occur. In this work, we introduce a novel interpersonal effectiveness improvement framework (ELAINE) that combines Artificial Intelligence and Virtual Reality to create a highly immersive and efficient learning experience using avatars. We present findings from a study that uses this framework to measure and improve the interpersonal effectiveness of individuals interacting with an avatar. Results reveal that individuals with deficits in their interpersonal effectiveness show a significant improvement (p < 0.02) after multiple interactions with an avatar. The results also reveal that individuals interact naturally with avatars within this framework, and exhibit similar behavioral traits as they would in the real world. We use this as a basis to analyze the underlying audio and video data streams of individuals during these interactions. We extract relevant features from these data and present a machine-learning based approach to predict interpersonal effectiveness during human-avatar conversation. We conclude by discussing the implications of these findings to build beneficial applications for the real world

If I Can\u27t Predict My Future, Why Can AI? Exploring Human Interaction with Predictive Analytics

This research study seeks to understand how AI-based chatbots can potentially be leveraged as a tool in a PSYOP. This study is methodologically driven as it employs validated scales concerning suggestibility and human-computer interaction to assess how participants interact with a specific AI chatbot, Replika. Recent studies demonstrate the capability of GPT-based analytics to influence user’s moral judgements, and this paper is interested in exploring why. Results will help draw conclusions regarding human interaction with predictive analytics (in this case a free GPT-based chatbot, Replika) to understand if suggestibility (how easily influenced someone generally is) impacts the overall usability of AI chatbots. This project will help assess how much of a concern predictive AI chatbots should be considered as virtual AI influencers and other bot-based propaganda modalities emerge in the contemporary media environment. This study uses the CASA paradigm, medium theory, and Boyd’s theory of conflict to explore how factors that often drive human computer interaction— like anthropomorphic autonomy and suspension of disbelief— potentially relate to suggestibility or chatbot usability. Overall, this study is interested in specifically exploring if suggestion can predict usability in AI chatbots

Immersive Participation:Futuring, Training Simulation and Dance and Virtual Reality

Dance knowledge can inform the development of scenario design in immersive digital simulation environments by strengthening a participant’s capacity to learn through the body. This study engages with processes of participatory practice that question how the transmission and transfer of dance knowledge/embodied knowledge in immersive digital environments is activated and applied in new contexts. These questions are relevant in both arts and industry and have the potential to add value and knowledge through crossdisciplinary collaboration and exchange. This thesis consists of three different research projects all focused on observation, participation, and interviews with experts on embodiment in digital simulation. The projects were chosen to provide a range of perspectives across dance, industry and futures studies. Theories of embodied cognition, in particular the notions of the extended body, distributed cognition, enactment and mindfulness, offer critical lenses through which to explore the relationship of embodied integration and participation within immersive digital environments. These areas of inquiry lead to the consideration of how language from the field of computer science can assist in describing somatic experience in digital worlds through a discussion of the emerging concepts of mindfulness, wayfinding, guided movement and digital kinship. These terms serve as an example of how the mutability of language became part of the process as terms applied in disparate disciplines were understood within varying contexts. The analytic tools focus on applying a posthuman view, speculation through a futures ethnography, and a cognitive ethnographical approach to my research project. These approaches allowed me to examine an ecology of practices in order to identify methods and processes that can facilitate the transmission and transfer of embodied knowledge within a community of practice. The ecological components include dance, healthcare, transport, education and human/computer interaction. These fields drove the data collection from a range of sources including academic papers, texts, specialists’ reports, scientific papers, interviews and conversations with experts and artists.The aim of my research is to contribute both a theoretical and a speculative understanding of processes, as well as tools applicable in the transmission of embodied knowledge in virtual dance and arts environments as well as digital simulation across industry. Processes were understood theoretically through established studies in embodied cognition applied to workbased training, reinterpreted through my own movement study. Futures methodologies paved the way for speculative processes and analysis. Tools to choreograph scenario design in immersive digital environments were identified through the recognition of cross purpose language such as mindfulness, wayfinding, guided movement and digital kinship. Put together, the major contribution of this research is a greater understanding of the value of dance knowledge applied to simulation developed through theoretical and transformational processes and creative tools

The Interaction of Cyberaggression and Self-Efficacy within the Virtual World and the Real World

The present study seeks to analyze the impact of cyberaggression and positive feedback from an anonymous videogame player on one’s self-efficacy and performance both inside and outside of the videogame. The internet provides a unique way for individuals to interact, and the online disinhibition effect can lead users to engage in out of character behaviors once online. This shift in behavior can be an influencing factor for cyberbullying or isolated instances of cyberaggression. Negative feedback can lower one’s self-efficacy, and a lower self-efficacy can lead to a worse performance on the activity. It was hypothesized that mean comments from an anonymous competitor would lower self-efficacy both in the game and for an unrelated memory task, and similarly diminish the performance in both activities. It was also hypothesized that a positive comment after the first competition would both increase self-efficacy inside and outside the game and also improve performance on both activities. Participants in the present study took a memory test, played an online racing game, received predetermined feedback after losing the race, then played the videogame and took the memory test one final time, after rating their self-efficacy before every activity. It was discovered that the type of message received did not play a role on self-efficacy and performance both inside and outside of the videogame

Attention and Social Cognition in Virtual Reality:The effect of engagement mode and character eye-gaze

Technical developments in virtual humans are manifest in modern character design. Specifically, eye gaze offers a significant aspect of such design. There is need to consider the contribution of participant control of engagement. In the current study, we manipulated participants’ engagement with an interactive virtual reality narrative called Coffee without Words. Participants sat over coffee opposite a character in a virtual café, where they waited for their bus to be repaired. We manipulated character eye-contact with the participant. For half the participants in each condition, the character made no eye-contact for the duration of the story. For the other half, the character responded to participant eye-gaze by making and holding eye contact in return. To explore how participant engagement interacted with this manipulation, half the participants in each condition were instructed to appraise their experience as an artefact (i.e., drawing attention to technical features), while the other half were introduced to the fictional character, the narrative, and the setting as though they were real. This study allowed us to explore the contributions of character features (interactivity through eye-gaze) and cognition (attention/engagement) to the participants’ perception of realism, feelings of presence, time duration, and the extent to which they engaged with the character and represented their mental states (Theory of Mind). Importantly it does so using a highly controlled yet ecologically valid virtual experience