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