[Closed] Call for papers for a Special Issue on “Learning Experience Design: Embodiment, Gesture, and Interactivity in XR”
The concepts of embodiment and embodied learning are gaining traction in the field of education, these concepts are deeply rooted in theories of Embodied Cognition (Barsalou, 2008; Wilson, 2002). New educational technologies enable researchers and practitioners to include more gestures and body movements into learning designs, creating immersive and gesture-rich learning environments (Georgiou & Ioannou, 2019; Dede, 2009; Johnson-Glenberg, 2018; Lindgren & Johnson-Glenberg, 2013; Lindgren, Tscholl, Wang, & Johnson, 2016; Minocha, Tudor & Tilling, 2017). Such embodied environments should enable multi-modal and multi-sensory forms of interaction through gestures and bodily movement, tactile and auditory sensory experiences. While the interplay of new forms of technology and learning is complex, recent evidence suggests that learning experience design, pedagogy, and practice with embodied learning technologies can have an important effect on learning, engagement, and achievement in all educational settings -- formal, non-formal and informal. This special issue aims to synthesize current knowledge on the design and evaluation of learning in immersive and embodied environments. The aim is to provide insights on best practices for learning design based on systematic or empirical data and analysis on learning outcomes or processes.
The specific scope is to publish research that addresses learning in immersive and embodied environments. The focus is not the technology per se, but rather issues related to learning design, the process continuum of learning, teaching, and assessment and how they are affected or enhanced using technologies, including gaming environments, escape rooms, VR and AR environments etc. We are not seeking theory papers or meta-analyses, but rather, evidence-based and impactful educational applications and research, meshing pedagogy and practice in these environments. The technology under consideration is augmented, virtual, or mixed reality (now called XR), and will include relevant work on haptics i.e., gloves, hacked controllers, or other tactile simulators, if they are used to further learning.
Cyprus University of Technology, Research Center on Interactive Media, Smart Systems and Emerging Technologies
Kaushal Kumar Bhagat
Centre for Educational Technology, Indian Institute of Technology, Kharagpur
Arizona State University, Embodied Games
[Closed] Call for papers for a Special Issue on “Precision Education - A New Challenge for AI in Education”
Precision education (Yang, 2019) is a new challenge of applying artificial intelligence, machine learning, and learning analytics for improving teaching quality and learning performance. The goal of precision education is to identify at-risk students as early as possible and provide timely intervention based on teaching and learning experiences (Lu et al., 2018). The precision education was inspired by the precision medicine initiative proposed by the former USA President Obama in his 2015 State of the Union address. The emergence of precision medicine is to revolutionize the one-size-fits-all treatment of disease by taking into account individual differences in people’s genes, environments, and lifestyles, as well as by improving the diagnosis, prediction, treatment, and prevention of disease.
Similar to medicine, the current education system is designed not fully considering students’ IQ, learning styles, learning environments, and learning strategies. Inspired by precision medicine, precision education is an innovative approach to emphasize the improvement of diagnosis, prediction, treatment, and prevention of at-risk students, such as diagnosis of students’ engagement, learning patterns and behavior; prediction of students’ learning performance; treatment and prevention with teachers’ timely intervention and well-designed pedagogy, learning strategy, and learning activities. In this special issue, at-risk students are confined to students who were diagnosed could get low academic performance, drop/withdraw a course, or students who were low engaged in terms of learning behaviour, emotion, and cognition.
Stephen J.H. Yang
National Central University, Taiwan
Kyoto University, Japan