The COVID-19 pandemic has prompted unprecedented attention to the social divides in educational access via online and blended learning (e.g., Greenhow et al., 2020, Technology, Pedagogy and Education). The unique affordances and challenges of online learning, along with its psychosocial correlates, have received more attention than ever before.

Although much opportunistic research has been conducted since the onset of the pandemic, these research efforts have consisted mostly of snapshot studies from a very specific and ungeneralisable moment in time. There is a dearth of research on students’ experiences of online education at the process-level, such as for social interaction. There is even less on teachers’ experiences of delivering online education at the process level (Rasheet et al., 2020, Computers & Education, systematic review).

Accordingly, we seek to convene a special issue of pioneering research that employs longitudinal research methodology to obtain insights into relational dynamics as explanations for divergences in learner experiences and gains from online and blended learning.

The longitudinal approach may be applied at one or all of the following levels: at the micro-level with measures made from moment to moment; at the meso-level with measures taken over days, weeks or months; or at the macro-level with measures taken over years. Longitudinal data can be collected from either qualitative approaches (e.g., multi-day diary studies), quantitative approaches (e.g., experience sampling survey techniques), or a combination of both. Multiple data, such as self-reported data and the results of empirical treatment, should be collected and analyzed to triangulate the data and increase research validity.

Relational dynamics may be interpersonal, power-related, related to social class, related to cross-cultural differences of communication (e.g., intercultural education), or another construct that authors demonstrate to have significant potential for impact on digital divides.

Studies focused on any stage of educationーfrom early years to higher educationーare welcome.

Guest Editors:

Dr. Nora McIntyre

School of Education, University of Southampton, UK

Dr. Eloise Symonds

Insendi Research Centre, Insendi Ltd., London, UK

May 2, 2021

Call for papers for a special issue on “Contextualized Multimodal Language Learning

All interactions are multimodal by nature (Norris, 2004). Humans use various modes to represent meanings and exchange ideas in communication. Multimodal language learning is coined upon the fact that humans integrate multiple modalities including audio, textual, gestural, visual, and spatial resources to learn languages. In second and foreign language learning, skills in interpreting mediated modalities are therefore necessary for language learners, especially when communication takes place in a cross-boundary and cross-cultural context. Digital technologies that allow the combined use of texts, images, audios, videos, and multimedia further intensify these so-called “multimodal possibilities” (Lotherington & Jenson, 2011, p. 227). Although multimodality provides great potential for virtual exchanges in digital platforms, online multimodal interactions can easily overwhelm second language learners (Abrams, 2016; Hampel & Hauck, 2006). With the multimodal affordances of the digital tools, the effects of multimodality on language learners’ meaning-making and language learning in technology-enhanced language learning (TELL) contexts thus deserve further explorations.

As Bax (2003) has foreseen the technology to be progressed towards normalization almost 20 years ago, technology nowadays has become invisible, embedded in everyday practice, and truly integrated into our lives. Language learners are engaged in technology-mediated practices not only in formal schooling but in digital wilds which go beyond contexts within educational settings (Sauro & Zourou, 2019). Language learning in ‘the wilderness’ in online or virtual contexts is rather open, dynamic, multi-faceted, and unpredictable. To have a comprehensive understanding of learners’ multimodal practice and performance in TELL activities, it is essential to contextualize learners’ multimodal language learning and examine the interrelationship between the multimodal potentialities of the online setting, the mediated human activity within that setting, and the characteristics of the learners. The interconnectedness between contexts, TELL activities, and learners thus become more complex and multi-faceted in the digital wilderness. Some questions arise: How does the multimodality feature affect language learning and language use in different online or virtual contexts? How to leverage technologies to foster learners’ multimodal language learning?

Guest Editors:

Mei-Ling Liaw

National Taichung University of Education, Taiwan

Hsin-I Chen

National Taipei University of Technology, Taiwan

April 19, 2021

With the world is becoming more complex and unpredictable, learners should acquire the basic skills to deal with it. The thinking processes associated with and the problem-solving approach of Computational Thinking (CT) allows learners to better deal with the complexity and open-ended non-trivial problems posed by the world and its emerging technologies (e.g., AI, big data, etc.). Therefore, several research studies advocated considering CT as an essential competence that should be included in all educational levels and in every student’s skill set (Grover & Pea, 2018), as part of pathways to provide CT education and literacy. According to the International Computer and Information Literacy Study (ICILS) 2018, CT can be defined as the “ability to identify a problem, break it down into manageable steps, work out the important details or patterns, shape possible solutions and present these solutions in a way that a computer, human or both can understand” (PISA, 2019).

However, despite the increasing attention towards CT in education, it still has several gaps, from different perspectives, that should be addressed to better understand and advance this field. For example, in a recently published review, Tikva and Tambouris (2021) found that CT curricula are poorly conceptualized. In line with this, Lee et al. (2020) reported that several STEM classrooms are failing to integrate CT into their curricula. De Jong and Jeuring (2020), on the other hand, conducted a recent review of CT in Higher Education and revealed that more investigation is needed to identify the set of CT skills and the assessment methods to measure these skills. Additionally, Lyon and Magana concluded that interest in CT is growing, but there is a need for more concrete definitions and implementations (Lyon & J. Magana, 2020). Particularly, there are a number of challenges in ensuring that computing curricula, tools and environments embody appropriate progression and engender motivation for the topic across the years (Howland et al., 2019).

Guest Editors:

Dr. Ahmed Tlili

Smart Learning Institute of Beijing Normal University, China

Dr. Daniel Burgos

Universidad Internacional de La Rioja (UNIR), Spain

Dr. Chee-Kit Looi

National Institute of Education, Nanyang Technological University, Singapore

January 3, 2021

This special issue will focus on underlying research with the use of AI (Artificial Intelligence) on augmenting human intelligence with machine intelligence in education, where the new design methods and tools can be leveraged and evaluated, hopes to advance AI research, education, policy, and practice to improve the human condition for education.

The advance of AI in decision-making, prediction, knowledge extraction, and logic reasoning has been making a wider impact on society, economy, and environment (Luan et al., 2020). AI has the potential to educate, train, and augment human productivity, making them better at their tasks and activities. AI can also make a better quality of an individual’s work, resulting in better learning and teaching. With proper use of AI, it can enable human welfare in many ways such as improving the productivity of food, health, water, education, and energy services, but the misuse of AI due to algorithm bias and lack of governance could inhibit human rights and results in jobs, gender, and racial inequality (Vinuesa, et al., 2020).

Human-centered AI can be interpreted from two perspectives, one is AI under human-control (Shneiderman, 2020), and the other is AI concerning human condition (Stanford HAI, 2020). AI under human-control is to leverage the collaboration between human-control and AI-automation to empower human productivity with a high level of reliability, safety, and trust. AI concerning human condition is that AI algorithms taking humanity as the main consideration, require explainable and interpretable computation and judgment process, as well as continuously adjust AI algorithms through human context and societal phenomena to augment human intelligence with machine intelligence, thereby enhancing the welfare of human kinds.

Guest Editors:

Stephen J.H. Yang

National Central University, Taiwan

Hiroaki Ogata

Kyoto University, Japan

Tatsunori Matsui

Waseda University, Japan

December 31, 2020

Call for papers for a special issue on “Learning at the Intersection of Data Literacy and Social Justice

Data is ingrained in our day-to-day lives. While there are many examples of how it is used for public good, the destructive impacts of unregulated uses of algorithms and big data are felt across sectors including education, law enforcement, healthcare (O’Neil, 2016). It is such that issues of data literacy are also issues of social justice, and education researchers have a role to play in developing data literate citizens (Raffaghelli, 2020).

Data literacy involves skills such as manipulating data sets, selecting and applying appropriate analyses, and making data-based inferences and arguments (Common Core State Standards Initiative, 2010; Franklin et al., 2007; NGSS Lead States, 2013); but it also involves a critical understanding of what and how data are produced and tracked, of how they can be used for particular purposes, and of the role of context in interpretations of data. Such a critical lens is essential for recognizing how data reflect the biases inherent in the systems that create and use them (Kitchin, 2014) how data are subject to multiple (mis)interpretations (Pangrazio & Selwyn 2018), and how data can echo power relations in society (Van Wart, Lanouette & Parikh, 2020). Without such abilities, there is a risk of reproducing societal inequities; and of further entrenching those in power, and those most vulnerable (O’Neill, 2016; Philip et al., 2013).

This special issue will build on recent research on the role of data literacy education for promoting social justice (Raffaghelli, 2020). In particular, we seek research that examines the transformative potential in the intersection of data literacy, education, and social justice, including racial justice, economic justice, environmental justice, and spatial justice. Together, we expect the contributions to this issue to examine such questions as:

1. What dimensions of learning (e.g., technical, socio-political) are most important in a complete account of critical data literacy? How can we best support learners’ growth in these dimensions?

2. How can data literacy education empower people, individually and collectively, to be agentic in both their local and global communities?

3. How can learning experiences be created that make visible each of the personal, community, and societal interests embedded in data?

More broadly, we anticipate that this special issue will offer examples of designs and frameworks that other researchers may use to align their work—whether or not their primary focus is explicitly on social justice—with social justice values.

Guest Editors:

Camillia Matuk

New York University

Simon Knight

University of Technology, Sydney

Kayla DesPortes

New York University

November 26, 2019

General Call for Special-Issue Proposals

Educational Technology & Society (ET&S) welcomes special issue proposals on specific themes or topics that address the usage of technology for pedagogical purposes, particularly those reflecting current research trends through in-depth research.

For more information, please visit the Special Issue Proposals page.

The ET&S Editorial Office

November 11, 2019

Why re-open the operation of ET&S journal?

After the suspension of ET&S journal, the editorial office continues to receive different types of requests, things like people reporting plagiarism issues, people asking for certificates of contributions, publishers/libraries requesting permissions for archiving certain articles or reproducing a specific diagram, potential authors keep sending manuscripts for reviews and various types of queries about the journal matters, etc.

It is not easy to really end a journal which has 22 years of history, at least not from the surface level. This triggers the editorial office to reconsider resuming the operation of ET&S journal and to establish a more sustainable operation model. A stable funding source and enough manpower are the two essential conditions to keep ET&S a fully open access journal. Furthermore, having a well-formed operational guideline is crucial to make the editorial office systematically running and to achieve its sustainability.

The ET&S journal has established a solid and stable editorial office with the support of National Yunlin University of Science and Technology. The new Editors-in-Chief have been appointed aiming to promote innovative educational technology research based on empirical inquires to echo the pedagogical essentials of learning in the real world—lifelong learning, competency-orientation, and multimodal literacy in the 21st century.

The ET&S Editorial Office