JCAL deals with all aspects of learning and computers, no matter how large or small. As such it publishes articles on the use of computers FOR learning, IN learning, IN teaching, IN learning assessment, etc. be they quantitative and/or qualitative empirical articles, review articles, conceptual articles, design articles, special issues, registered reports, and any other type of article at the crossroads of computers, learning, and teaching.

JCAL (Journal of Computer Assisted Learning) is a high-quality international peer-reviewed scientific journal which covers the whole range of uses of information and communication technologies to support learning, teaching, and instructional design & development. This can run from using simple apps at home or in the classroom to large language models for teaching and learning. It strives to function as both a medium for communication amongst researchers as well as a channel linking researchers, practitioners, and policy makers.

JCAL operates on the cusp of educational psychology, the learning sciences, educational & instructional technology, instructional design, collaborative learning, intelligent learning systems, learning analytics, open, distance and networked learning, and educational evaluation and assessment.

JCAL publishes studies on all types of learning: formal (pre-school, primary, secondary, and tertiary education), non-formal in the workplace or non-credit environments (e.g., MOOCs), and informal (e.g., museums, libraries, clubs). These articles can be quantitative and/or qualitative empirical, reviews, meta-analyses, conceptual studies, or design articles as long as they deal with computers and learning

JCAL also welcomes Special Issues offering a broad and in-depth perspective on a specific topic as well as State-of-the-Art on specific topics.

JCAL differs from other journals in that it welcomes and encourages high-quality submissions that might not have led to statistically significant results (a statistically non-significant result can be a very significant and important result) as well as replication studies.

Furthermore, the journal supports open science practices. We therefore welcome authors submit registered reports and articles with open research intentions.

In sum, JCAL welcomes:

    Empirical reports, either single studies or a programmatic series of studies, on the use of computers and information technologies in learning and assessment
    Critical and original reviews or meta-analyses on computers for learning, teaching, and education in general
    Studies on the design, development, implementation, and usability of innovative technology-based systems for learning, study, and teaching
    Empirical or conceptual studies on educational policy and improvement through the use of information and communication technologies
    Replication studies or registered reports
    High-quality studies that might not have statistically significant results

Keywords

    Artificial intelligence
    Augmented reality
    Blended learning
    College
    Computer assisted learning
    Computer supported collaborative learning
    Computers
    Distance education
    E-learning
    Education
    Electronic learning environments
    Elementary education
    Elementary school
    Games
    Gamification
    High school
    Higher education
    Information and communication technology
    Information systems
    Information technology
    jcal
    JCAL
    Journal of computer assisted learning
    Learning analytics
    Learning design
    Learning management systems
    Learning systems
    Mobile learning
    Multimedia
    Online learning
    Open science
    Open education
    Registered report
    Replication studies
    Secondary education
    Simulations
    Technological pedagogical content knowledge
    Tertiary education
    TPACK
    University
    Virtual reality
    Workplace learning

Special Issue on Dynamics of Learning and Learners in the Changing Digital Landscape
截稿日期: 2026-01-31

Learning as a psychological phenomenon involves the complex interplay of cognitive, behavioral, and emotional processes with contextual, and social factors. Such multifaceted nature makes learning far from being simple to directly capture, measure or observe which makes fit the salient features of a complex dynamic system (CDS) (Kaplan and Garner 2020; Hilpert and Marchand 2018; Saqr and López-Pernas 2024). Still, learning manifests in behaviors and dynamics that can be observed as students navigate learning environments, engage with learning resources or interact with peers and educators. These learning environments, the way learners interact with each other and with the learning content, are changing, and so are these dynamics (Hilpert and Marchand 2018; Saqr and Peeters 2022; Symonds et al. 2024).

The past two years have witnessed a vast transformation marked by the emergence of new technologies, communication methods and Artificial Intelligence (AI) (Noy and Zhang 2023). Such a change has impacted learning, teaching, and assessment. We see changes in the tools learners use, the environments they navigate, the way they interact, the support they are offered, and the outcomes of all of these processes (Mustafa et al. 2024). Further, new data sources are emerging (e.g., multimodal and AI interaction data), as well as new analytical frameworks are being introduced (e.g., dynamic and probabilistic networks). Furthermore, changes are influencing how humans interact with each other. Instant messaging, social media, and collaborative online tools have transformed learner interactions by enabling real-time communication and breaking down geographical barriers, fostering a sense of connectedness. The integration of AI into these platforms — through chatbots, recommendation algorithms, and personalized content — further influences learning dynamics (Stadler, Bannert, and Sailer 2024; Kaliisa et al. 2025). Therefore, it stands to reason that studying learners and learning dynamics within these emerging — and changing — digital environments is essential to understand such a fast-changing landscape (Saqr and López-Pernas 2024).

The fast-paced technological developments and the growth in data-intensive methods have often been accompanied by a lag in theoretical integration. Ensuring theoretical alignment is essential for both practical and scientific reasons. In the absence of such grounding, we may risk reporting complex patterns that are detached from pedagogical practice and won’t help advance our understanding of learning. Therefore, it is important that researchers studying learning dynamics adopt a sound theoretical framework that reconciles existing knowledge, bridges established theories, and also allows for the advancement of novel theories. In the same way, navigating the new realities and complexities of human-human, human-technology, and human-AI interaction commands new tools and methods that prioritize dynamics and processes over causation and comparisons (Saqr et al. 2024).

Methods that shift the focus from isolating individual variables to understanding how systems operate as a whole are necessary. Such methods, such as complex dynamic systems, capture the multifaceted nature of learning as an adaptive process characterized by emergent phenomena and self-organizing behaviors and offer a fertile ground for studying the intricate dynamics that are otherwise intractable by traditional methods. Methodologies that focus on temporality, feedback loops, adaptability, emergence of patterns, and non-linear trends. Most importantly, researchers should ground their work in well-established learning theories and underpin their approaches with state-of-the-art, robust research grounded in learning sciences (Kaplan and Garner 2020; Hilpert and Marchand 2018).

We invite contributions that examine the dynamics of learners and learning behavior with a particular emphasis on connecting data-driven insights and novel methods to foundational theories in educational psychology. While novel data-driven approaches provide rich opportunities to trace and model how learners engage over time, there remains a gap between research findings and a psychologically grounded understanding of learning processes. To advance our knowledge, researchers must move beyond descriptive accounts of learning dynamics and move towards a theoretically informed interpretation that connects novel methods with established theories or advances existing ones.

As such, this special issue welcomes contributions on these topics:

    Research that advances, refines, or develops theoretical frameworks to better understand human-human, human-technology, and human-AI interactions;
    Research that investigates the temporal dynamics of learning interactions using methodologies such as transition network analysis, temporal networks, or psychological network modeling;
    Research employing longitudinal designs that trace the development of learner behaviors and interactions across time;
    Research that conceptualizes learning as a dynamic psychological process, capturing its evolution across various time scales;
    Research focusing on human-AI interactions, analyzing how learners’ cognitive, emotional, and behavioral responses evolve through engagement with AI systems. 

Guest Editors:

Prof. Mohammed Saqr (Lead)
University of Eastern Finland,
Finland

Prof. Dragan Gašević
Monash University,
Australia

Prof. Petter Holme
University of Aalto,
Finland

Dr. Sonsoles López-Pernas
University of Eastern Finland,
Finland

Dr. Kamila Misiejuk
FernUniversität in Hagen,
Germany

Prof. Daryn Dever
University of Florida,
United States

Keywords:

Computer science; social connectedness; social media.

Special Issue on Informal Digital Learning of English (IDLE) as Innovative Pedagogy: Mapping Current and Future Trends
截稿日期: 2026-01-31

The rapid advancement of digital technologies, particularly generative artificial intelligence (GenAI), has profoundly influenced many dimensions of human activity, including English language teaching (ELT) (Liu et al., 2024; Liu et al., 2025a; Reinders & Benson, 2017; Reinders et al., 2022). This digital shift has coincided with the emergence of the fourth stage of Computer-Assisted Language Learning (CALL), often termed “ecological CALL” (Chun, 2019), which emphasizes learners’ engagement with diverse technological environments beyond traditional classrooms. One significant manifestation of this development is the increasing prevalence of Informal Digital Learning of English (IDLE; Lee, 2021).

As learners actively negotiate the affordances of digital tools and platforms, IDLE has emerged as a key site of English language development outside formal educational contexts. A growing body of research has documented the multifaceted benefits of IDLE for learners’ affective, cognitive, psychological, linguistic, and intercultural development (e.g., Barkati et al., 2024; Lee & Taylor, 2024; Liu et al., 2025b; Rezai et al., 2024; Soyoof et al., 2023; Soyoof et al., 2024).

Recent studies have identified a range of factors that shape learners’ engagement with IDLE, including demographic variables (e.g., gender), learner-internal characteristics (e.g., grit), and contextual influences (e.g., educational systems). These findings underscore the pedagogical potential of IDLE, particularly in EFL (English as a foreign language) contexts where access to English instruction may be limited. Despite its growing relevance, however, IDLE remains under-theorized and under-integrated within pedagogical frameworks. While its affordances for promoting language development, learner autonomy, motivation, and identity formation are increasingly recognized, a significant gap persists in understanding how educational professionals—teachers, teacher educators, and curriculum developers—can systematically incorporate learners’ IDLE experiences into formal instructional design and classroom practices.

Addressing this gap aligns directly with JCAL’s focus on the intersection of educational technology, learning sciences, and educational psychology. This special issue seeks to bridge this gap by examining the pedagogical, sociocultural, and theoretical dimensions of IDLE in education. We aim to advance research and practice on how educational professionals can evaluate, respond to, and leverage learners’ informal digital practices in ways that are equitable, contextually grounded, and pedagogically robust. The issue will contribute to JCAL’s mission of connecting researchers, practitioners, and policy makers by exploring innovative technology-based systems that support learning across formal, non-formal, and informal contexts.

We welcome diverse empirical studies, conceptual frameworks, and critical reviews that interrogate IDLE as a site of English language learning, identity formation, and social participation, with particular interest in implementation studies and learning analytics approaches that evaluate technology-mediated informal learning. We especially encourage submissions focusing on under-resourced or EFL settings where formal English learning opportunities are limited, supporting JCAL’s commitment to educational improvement through information and communication technologies.

Topics for this call for papers include but are not restricted to:

    How learners participate in informal digital learning of English (IDLE) across contexts
    How IDLE is understood and utilized by language teachers
    How IDLE is enacted in the contexts of emerging technologies
    How IDLE interacts with other individual differences factors to update our understanding of informal language learning in second language development

Guest Editors:

Prof. Ju Seong Lee
Education University of Hong Kong
China

Prof. Ali Soyoof
Education University of Hong Kong
China

Dr. Guangxiang (Leon) Liu
The Chinese University of Hong Kong
China

Keywords: AI-mediated informal learning; Computer assisted language learning; Informal digital learning of English; Innovating English language teaching; Language learning beyond the classroom