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Dear all,

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: 

The Deadlines:

19.10.2020 (Abstract)

18.10.2020 (Manuscript)

Regards (on behalf of the editorial board: Fridolin wild, Sheizaf Rafaeli,
Carmel Kent, and Amit Rechavi)

Amit Rechavi, Ph.D.
Data and Networks Research
The Department of Business Administration, Ruppin Academic Center
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HUJI Cyber Security Research Center
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