We are delighted to announce the research topic “Structural Shocks’ Implications on Learning Networks,” which we are organizing in Frontiers in AI, in the section on Learning and Behaviour Change. The goal of this Call for Papers topic is to gather empirical evidence about 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).
We are interested in understanding what can be learned when an existing equilibrium is shaken due to an unpredictable event as, for example, when millions of learners and teachers are placed in front of screens, distanced from each other, without 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 encourage but do not limit submissions to using AI and/or other computational techniques as well as mixed methods to harvest, process, predict and reflect on learning-related (social, content-based, or hybrid) networks and their manifestations as a response to unpredictable external changes. We invite manuscripts that explore changes in the learning structure, roles, outcomes, and behavior of individual learners and communities in learning contexts ranging from secondary schools to higher education and informal adult learning. More details can be found in the attached CFP and here:
Regards (on behalf of the editorial board: Fridolin wild, Sheizaf Rafaeli, Carmel Kent, and Amit Rechavi)
Amit Rechavi, Ph.D.