[Closed] Call for papers for a special issue on “Understanding and Bridging Gap in Multi-mode Digital Learning during Post-Pandemic Recovery”
At the doorstep of the third decade in the 21st century, fast-growing computing technologies boost the adoption of diverse devices and applications in the educational area, which dazzle instructors and learners. The outbreak of COVID-19 since the first month of 2020 made all learning activities online in many countries and territories, which adopted social distancing approaches to contain spread of virus. Unfortunately, many teachers and students have felt overwhelmed by such a drastic change in learning behaviour, though digital learning had been around for decades, including big efforts put in MOOC movements in different sectors. Quick adoption of digital means of learning and teaching which is the abnormality of teaching and learning could quickly fade out after the lifestyle get back to normal. It is interesting to know if the unexpected pandemic brings the yet-to-come education evolution earlier.
Although being competitive, the post-pandemic recovery is on the agenda. How the educational sector can stand with the contingency and bounce back stronger with insights gained during the pandemic pose interest. It pictures how the post-pandemic human development and learning looks like, allowing it to potentially shift from just content dissemination to augmenting relationships with teachers, personalization, and independence.
Evaluating the effectiveness and knowing in which environments the advanced technologies work better, and improving learning activities from both the students’ and instructors’ perspective are critical for the next generation delivery of the learning content. Given their comparative novelty, to what extent instructors and learners can accept and get accommodated to them sustain the ongoing update and development of new technologies. There are huge challenges ahead for understanding and bridging the gap in implementation of multi-mode digital learning over the coming decade.
University of Wollongong, Australia
Samuel Fosso Wamba
Toulouse Business School, France
University of Melbourne, Australia
[Closed] Call for papers for a Special Issue on “From Conventional AI to Modern AI in education- Re-examining AI and Analytics Techniques for Teaching and Learning”
There have been various definitions of the term artificial intelligence (AI) in the community of computer science. Different from “human intelligence,” AI refers to “computers that mimic cognitive functions that humans associate with the human mind, such as learning and problem-solving” (Russell, & Norvig, 2009, p. 2). Russell and Norvig (2009) argued that AI could be defined from the perspective of the intelligent agent, which can perceive the percepts from the external environment and take actions through the effectors to adapt to the environment changes or achieve certain goals. Moreover, Poole and Mackworth (2010, p.1) defined AI as “a system that acts intelligently: What it does is appropriate for its circumstances and its goal, it is flexible to changing environments and changing goals, it learns from experience, and it makes appropriate choices given perceptual limitations and finite computation.”
Although AI is not a new term, the meaning of modern AI has been changed compared to conventional AI techniques. Recently, modern AI is normally referred to the Deep Neural Networks (DNN) based techniques in recent years (Yosinski, Clune, Bengio, & Lipson, 2014). The DNN-based AI and analytic techniques have led to a significant evolution in both academic and industrial fields. With the rapid development of modern AI and analytics techniques like convolution neural networks (CNN), generative adversarial networks (GAN), reinforcement learning (RL), and so on, which are based on DNN paradigms, in recent years, there have been a huge number of innovative applications in various domains. For example, long short-term memory (LSTM) techniques have been exploited for predicting stock market prices (Sirignano, & Cont, 2019); CNN techniques have been adopted in surveillance systems, or self-driving cars (Hu & Ni, 2017; Chen, Ma, Wan, Li, & Xia, 2017) and RL methods have created some famous AI applications like Alpha GO (Silver et al., 2016).
Lingnan University, Hong Kong SAR
National Taiwan University of Sicence and Technology, Taiwan
Douglas College, Canada
[Closed] Call for papers for a Special Issue on “Creative Learning in Authentic Contexts with Advanced Educational Technologies”
Creativity is critical component of any learning programs because it is considered as the most important 21st century skills (Bryant, 2010; Lin, Shadiev, Hwang, & Shen, 2020; Rhodes, 1987; Shadiev, Huang, Hwang, & Liu, 2017; Sternberg & Lubart, 1999). Creativity is the ability to produce work that is original and useful; produced work can be intangible such as an idea or tangible such as an essay (Sternberg & Lubart, 1999). Creativity relates not only to the product that results from creative activity but also to the person who creates it, the cognitive processes involved in the creation of work, and the environmental influences (Mayer, 1989; Rhodes, 1987). Creative learning helps learners be innovative, learn new things, try out new ideas, and new ways of thinking and problem-solving. Scholars concluded that creativity is very important in today’s world of innovations and therefore, creative performance needs to be facilitated.
Authentic learning environments play crucial role in promoting creative skills development in learners (Davies et al., 2013; Jindal-Snape et al., 2013). An authentic environment here is defined as an environment that “preserves the complexity of the real-life context with rich situational affordances” (Herrington & Oliver, 2000, p. 180). Authentic learning environments contains a wide range of available resources that may stimulate learner creativity and make use of such resources supports the growth of ideas. Furthermore, authentic learning environments give learners greater freedom for imagination, provide rich contexts for the purpose of discovering learner schemas and interests. Scholars also argued that authentic contexts reflect the way that the knowledge will be used by learners in their real life (Herrington & Oliver, 2000; Shadiev, Hwang, & Huang, 2017). Therefore, creative learning in authentic learning environments need to be encouraged.
Nanjing Normal University, China
National Central University, Taiwan
Brunel University London, United Kingdom
[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 “Teacher Professional Development in STEM Education”
The term STEM (science, mathematics, technology and engineering) has become a buzzword among the global education practitioners who have called for curriculum reforms that will boost the competitiveness of the next generation by nurturing their problem-solving ability and creativity (Jane, Jong, & Chai, 2019). STEM education refers to “solving problems that draw on concepts and procedures from mathematics and science while incorporating the teamwork and design methodology of engineering and using appropriate technology” (Shaughnessy, 2013, p. 324). Simply put, it serves as a means to integrate different disciplines as used in tackling real-life problems. In the long term, this cross-disciplinary subject is expected to enhance students’ problem-solving, critical and analytical thinking skills, and cultivate them to be constructive and innovative citizens (Jong, 2015; Merrill, 2009).
The significance of STEM education in today’s technologically-dominated world cannot be underestimated. STEM competencies, nowadays, are not only required within but also outside of the STEM occupations (So, Jong, & Liu, 2020). In this regard, the development of students’ STEM competencies has become an urgent goal of many education systems around the globe, especially in K-12. The U.S. government has heavily invested in STEM education by implementing some state-level initiatives. For example, The “Educate to Innovate” initiative, launched in 2009, aims to enhance STEM literacy, improve teaching quality and increase educational and career opportunities for the youth through the collaboration between the government, the private sector and the non-profit and research communities (Burke & McNeill, 2011). In the U.K., the STEM education reform aims to ensure the provision of qualified people in the STEM workforce and the development of STEM literacy for the public (Department of Education and Skills, 2006). In Asian countries such as Korea, Hong Kong, Taiwan, China and Japan, STEM education has also emerged as an important curriculum reform (Ritz & Fan, 2015; So et al., 2020).
Morris S. Y. JONG
The Chinese University of Hong Kong, HKSAR
The Education University of Hong Kong, HKSAR
University of Michigan, USA
University of North Texas, USA
[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