By Michael Laudenbach (Carnegie Mellon University)
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While writing genres used in statistics remain understudied in rhetoric and composition, the best practices for assigning and assessing written texts in statistics and data science likewise remain opaque, perhaps owing to the elusive definition of data science curricula itself (Donoho 2017; Gere et al. 2016; Gooding et al. 2022). Working with the English Department and the Department of Statistics and Data Science, we have been analyzing linguistic data to identify tasks and genres that statistics & data science students would be expected to produce in the workplace or in academic research. This beyond session will demonstrate the use of Write & Audit, a student-facing text visualization tool developed at Carnegie Mellon University that we have been piloting in voluntary revision workshops with undergraduates. Students open their papers in the program, and their texts are automatically tagged according to predefined topics and expectations. These expectations are genre-specific, and rather than a structural outline for the paper, they convey rhetorical moves that typically appear at any point in the assigned project’s genre. In this case, we tailored the tool for an assignment that asks students to write a client- or user-facing paper, where statistical analyses are explained for the purposes of real-world decision-making.