Influence of interpersonal perception on information and knowledge sharing in e-learning networks

Abstract

This paper is one of a series describing elemental parts of a project on modelling information and knowledge diffusion in social networks. The projects objective is to develop methodological starting points and a prototype of an Agent-Based Simulation for simulating the spread of shared information in a social network. In this paper we describe an approach for specifying the difference between self-and interpersonal perception, which influences the sharing of information / knowledge through social networks. INTRODUCTIONAs we argued in [3], the sharing of information/knowledge between individuals and in groups, or even populations, can be considered a mayor stimulation for learning. While an information/knowledge transmission from 1-to-1 or 1-to-n is a common method for initiating learning processes, e.g. teacher-to-class in school, the transmission from n-to-m in social networks may be assumed to be at least equally important. This is even more the case for e-learning, where participants` activities are not bound by space or time, and/or in situations which focus more on knowledge generation than transmission. In both cases information sharing is not only important for stimulating learning but for inter and intra group coordination and administration, too. Furthermore the simulation of information diffusion may help to identify and select individuals for e-learning courses who will additionally spread the acquired knowledge in their social network vicinity. Thus reducing the number of required training courses for an enterprise or organization. But not only does the simulation of information/knowledge diffusion may reap benefits for the customer´s side, it provides mayor advantages for the organizations or enterprises that offer e-learning courses, too. The consideration and the optimization of the information flow can improve the learning and group experience of every customer. Thus creating a good reputation and increasing the probability of future customer contacts.

Resilience of Smart Power Grids to False Pricing Attacks in the Social Network

“…We normalize the scores generated from the above distribution in the range (0, 1): (11) where m o is the score generated from Equation 8 Each personality trait can affect an actor's willingness to diffuse information and, thus, affect the influence weight w i with which an actor wants to pass information to the successors [51].…”