Applying standard network analysis to hypermedia systems: Implications for learning

Hypermedia systems may be considered as networks of interconnected fragments of information. In the literature, several attempts have been made to describe characteristics of nodes, links, and of the overall structure of such systems. However, the utility of these attempts is only limited for educational purposes because most of them are uneconomic in measuring and almost none of them is explicitly concerned with learning. In this article, an analogy between information networks and social networks is established. This analogy is used to illustrate the application of standard network analysis procedures. These procedures are available in widespread PC software (e.g., UCINET) and are used for calculating indices mapping structural aspects of hypermedia systems which are hypothesized to be relevant for learning. It is shown how the graphical representation of a hypothetical hypermedia system can be transformed into a matrix format which functions as the basis for computing standard network indices for nodes, groups of nodes, and entire networks (e.g., among others, centrality, cliques, or path distances). For each index, its potential influence on learning processes or learning outcomes is discussed. Implications of using standard network indices for research and practice in the field of educational or instructional hypermedia systems are outlined.