Introduction: Students usually answer comprehension questions from texts as part of their academic activities. Elaborated Feedback (EF) has been found to be relatively effective to improve comprehension and learning from texts. However, there is little research on how computer-based feedback influences the question-answering process. This study examines the effects of a specific EF on the students' question-answering performance and their accuracy to assess textual relevance. Further, the study explores whether this EF influences the question, the text-search, and the feedback processing. Method: Seventy-five 7th and 8th grade students answered a set of 20 questions from two texts (10 questions per text). During the question-answering process, students had the text available and were forced to highlight the text information they considered relevant to answer each question. While half the students received item-based EF that included information on the student's answer correctness and her accuracy to select question-relevant text information along with monitoring hints on task-specific strategies, the other half received non-formative feedback (i.e., control feedback group). Results: EF enhanced the students' question-answering performance and reduced the amount of non-relevant text information students assessed as question-relevant. However, EF did not affect the question-relevant text information highlighted. Although EF did not influence the question and the text-search processing times in comparison to the control feedback group, students who received EF devoted more time and accessed additional information more often than the control group. Discussion and Conclusion: Computer-based EF on the students' question-answering performance and their accuracy to assess textual relevance can be efficient to improve the students' question-answering process. This study sheds light on how to deliver EF in a digital environment. However, further research is necessary to explore the cognitive and metacognitive processes involved in feedback processing.