E-Learning class formation will take benefit if common learners’ needs are taken into account. For instance, the availability of trust relationships among users can represent an additional motivation for classmates to engage activities. Common experience also suggests that there are many similarities within dynamics of formation for thematic social network groups and e-Learning classrooms. Since Online Social Networks provide data concerning users interactions (e.g. trust relationships), we propose a model aimed at managing the formation and the evolution of e-Learning classes based on information available on Online Social Networks — skills, interactions, and trust relationships — which are properly combined in a unique measure. The aim is to suggest both to a user the best classes to join with and to the classes themselves the best students to accept. The proposed approach has been tested by simulating an e-Learning scenario within a large social network. Experiments show that this proposal is able to support students and class managers in order to satisfy their expectations in a scalable manner.
Combining trust and skills evaluation to form e-Learning classes in online social networks / DE MEO, P; Messina, F; Rosaci, D; Sarne', G. - In: INFORMATION SCIENCES. - ISSN 0020-0255. - 405:C(2017), pp. 107-122. [10.1016/j.ins.2017.04.002]
Combining trust and skills evaluation to form e-Learning classes in online social networks
ROSACI D;SARNE' G
2017-01-01
Abstract
E-Learning class formation will take benefit if common learners’ needs are taken into account. For instance, the availability of trust relationships among users can represent an additional motivation for classmates to engage activities. Common experience also suggests that there are many similarities within dynamics of formation for thematic social network groups and e-Learning classrooms. Since Online Social Networks provide data concerning users interactions (e.g. trust relationships), we propose a model aimed at managing the formation and the evolution of e-Learning classes based on information available on Online Social Networks — skills, interactions, and trust relationships — which are properly combined in a unique measure. The aim is to suggest both to a user the best classes to join with and to the classes themselves the best students to accept. The proposed approach has been tested by simulating an e-Learning scenario within a large social network. Experiments show that this proposal is able to support students and class managers in order to satisfy their expectations in a scalable manner.File | Dimensione | Formato | |
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