This study examined the role of relevance determinations within the context of undergraduates' multiple text reading and writing. In this study, undergraduate students were randomly assigned to one of two experimental conditions (i.e., to compose a research report about either the causes of or the solutions to the urban housing crisis), using a library of 12 digital texts (six more relevant and six less relevant to students' assigned task condition). Guided by the Multiple Documents Task-Based Relevance Assessment and Content Extraction (MD-TRACE) model, we identified the key features that students included in their task models, used log data to profile students' text selection justifications and navigation, and categorized students' writing as task-relevant or not. As such, we found students' relevance determinations to play a key role in forming task models prior to text access, selecting and navigating texts during multiple text use, and composing a task product after accessing multiple texts. While we did not find students' initial task models to be associated with their patterns of text selection justifications or navigation nor with writing performance, we did find differences in writing performance across students belonging to different text selection justification and navigation profiles. Implications for theory and research on learning from multiple texts are discussed.