Social models in open learning object repositories: A simulation approach for sustainable collections

Abstract

Learning object repositories (LOR) are digital collections of educational resources and/or metadata aimed at facilitating reuse of materials worldwide. In open repositories, resources are made available at no cost, representing a case of information sharing with an implicit and diffuse social context. In such settings, quality control is in many cases based in some form of community filtering that provides a reliable basis for ranking resources when repositories reach a critical mass of users. However, there have been numerous repository initiatives and projects and many of them did not reached a significant degree of actual usage and growth that made them sustainable in the long term. In consequence, finding models for sustainable collections is a key issue in repository research, and the main problem behind that is understanding the evolution of successful repositories. This in turn requires analyzing experimental models of the behavior of their users that are coherent with the available evidence on their structure and growth patters. This paper provides a partial model for such behavior based on existing reported evidence and on the examination of patterns in a large and mature repository. Agent-based simulation was chosen to allow for contrasting configurations with different parameters. Simulations were devised with the RePast framework and the resulting model implementation constitutes an initial baseline for future studies aimed at contrasting empirical data on repository usage with their community setting. The model described accounts for known user contribution patterns and it is coherent with the implicit social network structure found in an existing large LOR.