Exploring affiliation network models as a collaborative filtering mechanism in e-learning

Abstract

The online interaction of learners and tutors in activities with concrete objectives provides a valuable source of data that can be analyzed for different purposes. One of these purposes is the use of the information extracted from that interaction to aid tutors and learners in decision making about either the configuration of further learning activities or the filtering of learning resources. This article explores the use of an affiliation network model for such kind of purposes. Concretely, the use of techniques such as blockmodeling - a technique used to derive meaningful patterns of relationships in the network - and the analysis of m-slices - a technique helpful to study cohesion in relationships - are explored as tools to decide on the configuration of topics and/or learner groups. In particular, the results of the case study show that such techniques can be used to (i) filter participants for rearranging groups; (ii) rearrange topics of interest; and (iii) dynamically change the structure of a course. The techniques presented can be considered a case of collaborative filtering based on social network structure. © 2011 Taylor & Francis.