Online "Peer Instruction" has become prevalent in many "flipped classroom" settings, yet little work has been done to examine the content students generate in such a learning environment. This study characterizes a dataset generated by an open-source, web-based homework system that prompts students to first answer questions, and then provide explanations of their reasoning. Of particular interest in this dataset, is that students are also prompted to evaluate a subset of peer explanations based on how convincing they are, as part of the Peer Instruction learning script. Since these student "votes" are then used in the selection of what is shown to future learners, we cast this as an instance of "learnersourcing," a paradigm that presents new research opportunities for the Learning Analytics community. This study characterizes a dataset from one "Peer Instruction" tool, that includes not only the student generated answers and explanations, but this novel "vote" attribute, which aims to capture how convincing each explanation is to other learners. The dataset includes longitudinal observations of student responses over the course of a semester, following groups from three STEM disciplines. The data is made available to interested researchers. [For the full proceedings, see ED607784.]