Topic modeling is widely used for content analysis of textual documents. While the mined topic terms are considered as a semantic abstraction of the original text, few people evaluate the accuracy of humans' interpretation of them in the context of an application based on the topic terms. Previously, we proposed RevExplore, an interactive peer-review analytic tool that supports teachers in making sense of large volumes of student peer reviews. To better evaluate the functionality of RevExplore,

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... in this paper we take a closer look at its Natural Language Processing component which automatically compares two groups of reviews at the topicword level. We employ a user study to evaluate our topic extraction method, as well as the topic-word analysis approach in the context of educational peer-review analysis. Our results show that the proposed method is better than a baseline in terms of capturing student reviewing/writing performance. While users generally identify student writing/reviewing performance correctly, participants who have prior teaching or peer-review experience tend to have better performance on our review exploration tasks, as well as higher satisfaction towards the proposed review analysis approach.