Information seeking in academic learning environments: an exploratory factor analytic approach to understanding design features

Traditional information retrieval (IR) systems perform retrieval by "closely matching" a query to a set of documents objectively without considering users\´ contexts. We aim to enhance objective relevance and address its limitations by taking a quantitative, subjective relevance (SR) approach. SR provides suitable theoretical underpinnings as it focuses on a document\´s relevance for users\´ needs. Our present work builds on an initial study where features supporting students\´ evaluations of subjective relevance of documents were elicited. Using digital libraries as an example of IR systems, we designed a survey form based on elicited SR features and conducted a quantitative, pilot study with 465 university students. The pilot study was exploratory and aimed to understand students\´ preferences for features as they completed a task in a digital library. Exploratory factor analysis (EFA) was used to analyze the data as it removed redundant features and identified relationships so that groups of important features supporting students\´ IR interactions could be discovered to provide rationale for designing IR interfaces supporting SR