In the educational context there is an increasing interest in learning networks. Recommendation systems can play an important role in achieving the educational objectives. Although we can find many papers focused on recommendation techniques and algorithms however, less attention has been dedicated to social factors that influence the recommendation process. This process could be improved if we had a deeper understanding of the social factors that influence the quality or goodness of a suggestion made by the recommendation system. This work elucidates and analyses the social factors that influence the design and decision making process of recommender systems. We conducted a survey where 126 undergraduate students were asked to extract are the main factors for improving suggestions when they are interacting with an Online Social Network (OSN) or in an Educational Social Network (ESN). The results show that different factors have to be considered depending on the type of the network.
Pablo A. Haya, Oliver Daems, Nils Malzahn, Jorge Castellanos and Heinz Ulrich Hoppe
British Journal of Educational Technology. Article first published online: 3 MAR 2015 (2015) DOI: 10.1111/bjet.12264 [download] (JCR, IF 2014: 1.394, Q1)
Learning Analytics constitutes a key tool for supporting Learning Design and teacher-led inquiry into student learning. In this paper, we demonstrate how a Social Learning Analytics toolkit can combine social network analysis and content analysis for supporting a global and formal teacher inquiry. This toolkit not only supports teachers in improving the organisation of the learning process but also generates important input to improve the students’ reflection on their own learning. Our examples show how combinations of different levels of analysis can provide deep insight in the learning process. We report a case study that exemplifies the main features of our approach and the kind of outcomes that can be obtained. Commenting and rating processes on videos are analysed based on user traces from a social learning platform. Finally, we point out implications on the learning design for networked learning environments in general.