About this Research Topic
All networks are sensitive to structural shocks and learning networks are no exception to this rule. Lockdowns, social distancing, and forced displacement are examples of sudden disturbance caused by events like the COVID-19 pandemic. Such interferences can reshape networks and community structures beyond recognition in a short amount of time. These shakings of an apparent equilibrium may accelerate long-needed educational transformations, but may also provoke undesirable changes, such as widening the achievement gaps between different groups.
Computer-mediated contexts of learning provide us with rich data about learning processes and behaviors, interaction networks of content, co-learners, educators, other educational stakeholders, and a wide range of learning outcomes. Artificial intelligence (AI), network analysis, and other quantitative and qualitative methods can create evidence on an (ongoing) change in learning networks. Such evidence is needed to inform pedagogy, policy, design, and social norms.
The goal of this topic is to gather empirical sound evidence on how learning networks respond to sudden external events. We are interested in what can be learned from data in computer-mediated learning with a particular focus on learning networks and communities as well as their associated outcomes (regarding learners’ communication, learning gains, and such). Learning networks can be designed in a top-down formal manner and formed serendipitously, and yet they can be equally effective. In either form of system equilibrium, we are interested in understanding what can be learnt when this equilibrium is shaken due to an unpredictable event as, for example, when millions of learners and teachers are put behind screens, remotely from each other, without almost any proper preparation and planning. What are the short-term and long-term effects of these disruptions? Can the systems return to their original state? Will a new equilibrium be reached and, if so, will it be designed or formed arbitrarily?
We are encouraging, but are not limiting submissions using AI and other computational techniques as well as mixed methods to harvest, process, predict, and reflect on learning-related (social, content-based or hybrid) networks and its manifestations as a response to unpredictable external changes. We invite manuscripts that explore the changes in the learning structure, roles, outcomes, and behavior of individual learners and communities in learning contexts ranges from secondary schools to higher education and informal adult learning. Topics of interest will investigate networks' change as a result of external shocks and include, but are not limited to theoretical aspects, field experiments, and case studies of:
● Social, content or hybrid network analysis, potentially complemented with machine learning, natural language processing, or qualitative methods to analyze or predict the change in patterns of learning processes and outcomes;
● Early detection and prediction of external shocks;
● Processes of structural compensation resulting from unpredictable events;
● Changes in the evaluation, assessment, and feedback mechanisms;
● Changes in the behavior of learning communities while transforming in and out of distance learning;
● Comparison of responses in various subject areas (such as STEM and humanities).
Keywords: Structural shocks, social networks analysis, learning, learning data and digital traces, COVID-19, pandemics, learning analytics
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